Being ideational rather than factual entities, they have a very long tradition as conceptual realities. In the history of dialectology, the existence of dialect areas has been a permanent presupposition since its very beginnings. The cognitive organisation of dialectal variation in terms of areas or varieties seems to be virtually inevitable, or at any rate very compelling, when dealing with language in space. This can be illustrated with a passage from Chambers & Trudgill (1998), who state that they use such categories because they are handy, although they convey a strictly inaccurate picture of how language varieties are organised (in space or otherwise):

We shall […] be using labels for linguistic varieties that may suggest that we regard them as discrete entities. It will be as well, nevertheless, to bear in mind that this will in most cases be simply an ad hoc device and that the use of labels such as 'language', 'dialect' and 'variety' does not imply that continua are not involved.

(Chambers & Trudgill 1998: 12)

Also Peter Wiesinger (1983: 807) sees a general need or propensity to group similar ways of speaking together, which pertains to both linguists and nonlinguists. Such groupings can be used, together with some salient linguistic features that are regarded as typical of them, to allocate speakers to a certain region. He stresses the practicality of regarding varieties as "discrete entities", as doing so makes it easier for speakers and for linguists alike to deal with the complexity of dialectal differences. Put more generally, dialect areas or varieties can be regarded as the expression of a mental requirement for categories, the result of a conscious or unconscious attempt to cognitively organise a large number of disparate but interrelated ways of speaking. Dialect areas are, like all kinds of categories, groupings of elements that are defined by certain traits.

There are different kinds of categories; one basic distinction often made is between crisp and fuzzy categories. Crisp categories are defined by certain traits or features that are either necessary or sufficient conditions. These categories make it very easy to decide whether a given element belongs to them or not: if the element has all the necessary or at least one of the sufficient features, then it is a member of the category. However, the definition of categories and their conditions might be regarded as arbitrary in the first place. Fuzzy categories, on

### 5 Fuzzy dialect areas and prototype theory

the other hand, are defined by a number of traits or features that serve as cues for that category, which are, however, neither necessary nor sufficient. If an element has many of the features associated with that category, then it is very likely that it belongs to it. Also the features themselves can have different degrees of importance for the category. Thus, no definitive answer is given as to whether an element belongs to a category or not; instead, membership is expressed as a gradual value or a probability.

Dialect areas, like other kinds of categories, can be modelled as crisp or fuzzy categories. The classical dialect area with sharp boundaries is a crisp category of local dialects; Girard & Larmouth (1993: 108–113), on the other hand, explicitly conceptualise dialect areas as "fuzzy sets", assigning local membership values between 0 and 1 to individual dialects. To obtain crisp dialect areas, specific defining features have to be selected. In this way, it is almost inevitable to pick those features that will reconstruct and thereby justify preconceived notions of areas. Therefore it is preferable not to preselect defining features, but to look at a large set of variables which may or may not be relevant. Dialect areas can then be delimited by looking for sets of bundling isoglosses. Depending on whether they coincide exactly or bundle together loosely, they delimit crisp or fuzzy dialect areas. This method is well-established in dialectology (cf. e.g. Hans Kurath 1972) and can be traced back to August Bielenstein (1892). Craig M. Carver (1987) used a similar approach for constructing dialect "layers" by combining features with similar geographic distribution. Also cluster analysis can be used to construct crisp or fuzzy dialect areas, depending on the method used (e.g. bootstrap clustering or noisy clustering; cf. Nerbonne et al. 2011: 83), without having to preselect defining features. Generally, it seems advisable to use tools that allow for fuzzy structures to emerge from the data and do not restrict the form of the outcomes to crisp structures. Later on in this paper, I will argue that factor analysis is a statistical tool particularly suited for identifying fuzzy dialect areas.

From a more theoretical perspective, viewing dialects against the background of fuzzy set theory (cf. Zadeh 1965) seems to provide a useful formalism for dealing with fuzzy dialect areas (cf. Girard & Larmouth 1993). Treating local dialects as elements that can have different degrees of belonging to an area implies that there are more and less typical examples of a dialect variety, which we could also call a dialect type. This way of treating dialects has also been used for describing how dialects are organised cognitively by members of the speech community.

Lectal categories, in short, constitute prototype categories. If lectal varieties constitute prototype categories, some realizations will be more 'typical' or

### Simon Pickl

'central' or 'better examples' of a given variety than others. (Kristiansen 2008: 59)

This is one example of a number of attempts to apply prototype theory (Rosch 1973; Lakoff 1987) to dialect geography, all of which are, as far as I see, folk linguistic approaches (Christen 1998; 2010; Berthele 2006; Kristiansen 2008; Pustka 2009) in the sense that they deal with how speakers conceptualise language in space. Perceptual dialectology has produced many new insights in the cognitive perspective of language geography in the past decades, spearheaded by Dennis Preston (cf. e.g. Preston 1989; 1999; Anders, Hundt & Lasch 2010). It appears that prototype theory is a useful framework to describe how dialects are organised cognitively, and it is compatible with fuzzy set theory (see, however, Kretzschmar 2009: 218–250, who is critical of using prototype theory and favours schema theory). Prototype theory assumes that cognitive concepts are fundamentally fuzzy: examples for a concept can be more or less typical, depending on their traits or features (cf. also Labov 1973). Consequently, a specific way of speaking can be a more or less typical example of a dialect type. Folk linguistic dialect types are emic categories; they are cognitive concepts of the speakers whose speech is at the same time the object of linguistic investigation.

There is no reason why the fundamental linguistic concept of a dialect variety should differ from the folk linguistic concept. In other words, scholarly or etic ideas about geolinguistic entities can and should have the same principal structure as lay persons' implicit ideas about dialects in space while being based on transparent – and, as far as possible, objective – criteria that are not derived from the speakers' ideas, but from scientific reasoning. Only in this way, the question of to what extent emic and etic dialect types coincide and why (not) can be tackled: In what way do some folk linguistic ideas of space diverge from linguistic ones, and why? It appears worthwhile to use a linguistic (etic) notion of dialect types that is similar to the folk linguistic (emic) one, but based on intersubjective criteria. This way, it becomes possible to compare emic and etic dialect types directly and identify how and why they differ.

If dialect varieties – emic or etic ones – take the shape of fuzzy categories, then they have no clear-cut boundaries or distinct features; instead, their spatial distributions are fuzzy, and local dialects are typical of them to varying degrees. Also, linguistic features are not simply features of one variety or the other, but they have different degrees of relevance for them. Thus, individual dialects can be better or worse examples of a variety, i.e. they have different values of typicality for a dialect type. Typicality is a measure for the graded membership of dialects, thus providing the structure of a fuzzy set. If typicality or membership values of

### 5 Fuzzy dialect areas and prototype theory

a dialect type are projected into space, the result is a graded or fuzzy dialect area; being fuzzy, it can overlap with other dialect areas. Linguistic features, on the other hand, can be better or worse cues for a dialect type, i.e. they have varying degrees of cue validity or feature validity. Consequently, the way in which dialect types are arranged in space takes the form of concretions with broad transition areas (cf. Figure 1). Note that there is no requirement for dialect types to have a core area in the sense that there must be locations that belong a hundred percent to them; instead, the core area of a dialect type can be defined as the area where it is dominant, i.e. the area where it is the dialect type with the highest local typicalities. There may even be dialect types that are dominant nowhere. Because dialect types overlap in space, they appear layered; the individual layers consist of congruent distribution areas of co-occurring linguistic forms.

Figure 1: Layer model of dialect types in space (adapted from Pickl 2013a: 70).

This is in line with Gaetano Berruto's definition of varieties, which is based on the simple assumption that when a number of linguistic variants tend to occur together, then these variants constitute a variety:

The tendential co-occurrence of variants gives rise to linguistic varieties. Therefore, a linguistic variety is conceivable as a set of co-occurring variants; it is identified simultaneously by both such a co-occurrence of variants, from the linguistic viewpoint, and the co-occurrence of these variants with extralinguistic, social features, from the external, societal viewpoint. (Berruto 2010: 229)

### Simon Pickl

This notion captures several of the usual requirements for varieties: their relative internal homogeneity and their relative mutual dissimilarity, and also their association with language-external factors.1 Varieties are thus condensations of co-occurring variants that can be pinned to a certain geographical expanse only to some extent – they are fuzzy and they overlap. Their arrangement in space is similar to the one depicted in Figure 1. According to Berruto, a variety as a condensation area is defined by certain co-occurring linguistic variants (its features). Depending on how many of these features are present in a given dialect, this dialect has a specific degree of membership between 0 (none of the features are present) and 1 (all of the features are present).

How are dialect types to be determined? Any method that is intended to identify linguistic varieties in the sense of Berruto's condensations will have to identify co-occurrences among linguistic variants. Craig M. Carver's (1987) approach did something similar in using lexical congruencies to establish 'layers' in American dialects,2 "essentially speech areas characterised by sets of words with a similar geographic distribution" (Boberg 2005: 24). The resulting structure is quite similar to what is illustrated in Figure 1.

The remaining part of this paper is dedicated to demonstrating how the theoretical idea of fuzzy dialect types can be implemented methodologically and practically. It is to be shown that taking such an approach does not only reproduce well-established geolinguistic structures in a more nuanced way, but also that it yields new insights, e.g. regarding weaker, non-dominant structures.

### **2 A tool for identifying dialect types**

There are various methods available for the identification of dialect areas (see Grieve 2014 for a more detailed comparison of popular statistical methods). Some of them, like fuzzy clustering, are suitable for identifying dialect types as fuzzy categories. However, I will argue that most of them are not suited for the identification of dialect types conceived of as layers of linguistic co-occurrence, either because of the structure of their outcomes or because of their internal working mechanisms, and that there are two options that are similarly well suited for this goal.

1 It does not capture, however, their emic status, as required by Auer (1986: 99) and Lenz (2003: 389–390). As I treat emic and etic varieties separately, focussing on etic varieties, this is consistent and does not pose a problem.

2 I would like to thank an anonymous referee for making me aware of this connection.

### 5 Fuzzy dialect areas and prototype theory

cluster analysis, the quantitative method that is to date the most popular tool to identify dialect areas (see e.g. Goebl 1983; Prokić & Nerbonne 2008; Prokić 2010: 17–29), analyses the aggregated similarities between local dialects to establish groups of local dialects that are relatively homogeneous internally and at the same time relatively distinct from each other. These groups or clusters are based on a measure of similarity between sites but not between distribution areas; it does not take into account the distribution patterns of individual variants and *their* mutual similarities, which would be a requirement for identifying condensations in Berruto's sense. cluster analysis does not identify types and their features, but clusters. For this reason, it is also impossible for a cluster analysis to come up with anything more subtle than global, exclusively dominant areas; subordinate, non-dominant areas that are determined by smaller numbers of features cannot be identified by cluster analysis. So, even though there are 'fuzzy' implementations of cluster analysis that yield overlapping clusters (e.g. bootstrap clustering or noisy clustering; cf. Nerbonne et al. 2011: 83), it is not a candidate for the operationalisation of dialect types. Bipartite spectral graph partitioning, which can also determine clusters of local dialects, simultaneously identifies the linguistic variants associated with these clusters (cf. Wieling & Nerbonne 2011) and is therefore in theory suitable for identifying areas together with their features. However, for our purpose this method has the disadvantage that it does not yield fuzzy areas but crisp clusters, at least as implemented by Wieling & Nerbonne (2011) or Wieling, Shackleton & Nerbonne (2013).

Multi-dimensional scaling (MDS) (see Wieling & Nerbonne 2015: 245 for an overview), "the de facto standard in dialectometry" according to an anonymous referee, arranges local dialects in a coordinate system of two or more dimensions, thus summarising the multiple similarities between local dialects. Again, the basis for the analysis are the linguistic similarities between sites, not the similarities between distribution areas. "MDS takes a site × site distance table as input and tries to assign the sites in the table to coordinates in a small-dimensional space, typically consisting of two or three dimensions" (Wieling & Nerbonne 2015: 245). Thus, it does not actually yield dialect areas but rather a dialect continuum without distinguishing condensations. Even if we took the axes as representing some sort of types, there would still be the problem, as with cluster analysis, that the results are based on global similarities between sites only, while similarities or differences between linguistic variants' spatial distributions are not taken into account. Therefore, the results of MDS cannot be interpreted as dialect types as discussed in the preceding section.

### Simon Pickl

Two methods that are similar in the form of their results, but not in their internal functioning, are promising candidates for identifying fuzzy dialect areas as dialect types. Both Principal Component Analysis (PCA) and Factor Analysis (FA)3 take feature × site matrices as data input and express recurring patterns in the data as principal components or factors, usually producing a principal component/factor × site matrix as output. Additionally, a principal component/factor × linguistic feature matrix can be calculated. One of the earliest applications of PCA/FA in linguistics comes from Douglas Biber, who used it to analyse stylistic variation in written texts.

In a factor analysis, a large number of original variables, in this case the frequencies of linguistic features, are reduced to a small set of derived variables, the 'factors'. Each factor represents some area in the original data that can be summarised or generalised. That is, each factor represents an area of high shared variance in the data, a grouping of linguistic features that co-occur with a high frequency. (Biber 1988: 79)

As a method for the reduction of high-dimensionality data, FA condenses the variation in a large data collection to a smaller number of underlying tendencies or factors. PCA does something very similar. By summarising large numbers of variants that have similar distributions, the variation in a data collection is condensed, providing a summary of predominant patterns in the data. Thus factors – "grouping[s] of linguistic features that co-occur with a high frequency" – or principal components are exactly what an operationalised method for identifying dialect types as condensations of co-occurring variants in the geographical dimension should output. The principal components or factors can be seen as condensations or layers because they are summaries of the distributions of cooccurring variants. Since co-occurrence is mathematically determined in terms of correlations, it is a technical requirement that the variant occurrences are given in the form of something like frequencies. Thus both PCA and FA meet the requirements of identifying linguistic layers as condensations of co-occurring variants and of yielding fuzzy areas as results. Hence, applying FA or PCA to geolinguistic data to find spatial patterns that qualify as varieties seems promising.

PCA and FA work quite similarly as far as their outcomes are concerned, but they function differently "under the hood". Both methods have been used several times before in dialectology.4 For the present purpose, FA is favoured over PCA because FA is less susceptible to random variation and therefore "a more

3 For a general introduction to both methods, see Tabachnick & Fidell (2012).

4 See, among others, Shackleton (2005); Hyvönen, Leino & Salmenkivi (2007); Szmrecsanyi &

### 5 Fuzzy dialect areas and prototype theory

suitable method for identifying co-occurring linguistic features" (Leinonen 2010: 106). Leino & Hyvönen, comparing different component models including FA and PCA in an application to Finnish data, found that FA "gave solid and easily interpretable results" (2008: 186) and could be used as a default method.

The implementation of FA for dialectometric analyses presented in the following section was developed in the DFG-funded research project *New Dialectometry Using Methods of Stochastic Image Analysis*5 (Department of German Linguistics, University of Augsburg, and Institute of Stochastics, Ulm University). It is included in *GeoLing – a software package for geolinguistic data*, which was developed in the project and is available as open source software (GPLv3) at www.geoling.net. The results reported in this article were obtained using this software.

### **3 Dialect types in Bavarian Swabia**

In this section, the approach outlined in the previous sections will be exemplified with data from the *Sprachatlas von Bayerisch-Schwaben* (SBS, König 1996–2009), a dialect atlas that covers an area in the south of Germany. The area of investigation is delimited by the administrative region of Swabia in the south-west of Bavaria plus some adjoining stretches in the north and east, minus a part in the south that is already covered by the *Vorarlberger Sprachatlas* (VALTS). The data were collected under the direction of Werner König in the form of dialect interviews that were conducted at 272 record locations. The published version of the SBS contains approx. 2,700 maps covering lexical, morphological and phonetic variables in 14 volumes. Per location and map, up to three different variants are documented.

In previous research from the project that reported results from FA (Pickl 2013a,b; 2014; Pröll 2015), the individual subsets (lexicon, morphology, phonetics) were analysed either separately or all combined. In this article, the morphological and phonetic subsets will be analysed together, excluding the lexical subset to provide an additional angle. The rationale behind this is that morphology and

Wolk (2011); Wieling, Shackleton & Nerbonne (2013) for PCA and e.g. Clopper & Paolillo (2006); Nerbonne (2006); Grieve, Speelman & Geeraerts (2011) for FA. Grieve (2009) and Leinonen (2010) use both, while Leino & Hyvönen (2008) compare PCA and FA with other component models. The approach and data used in this paper are based on previous research by Pickl (2013a,b; 2014); Pröll, Pickl & Spettl (2015) and Pröll (2015).

5 *Neue Dialektometrie mit Methoden der stochastischen Bildanalyse* (http://www.philhist.uni-augsburg.de/de/lehrstuehle/germanistik/sprachwissenschaft/ projekte/dialektometrie/)

### Simon Pickl

phonetics are usually seen to be more systematically organised and thus more relevant for geolinguistic abstractions (cf. Francis 1983: 20; Labov, Ash & Boberg 2006: 41, 119).6 For a more detailed comparison for FA based on different subsets, see Pröll (2015: 84–132); generally, he finds that morphological and phonetic variation can be slightly better summarised (61% and 64% explained variance, respectively) than lexical variation (57% explained variance).

The data for this study consist of 831 phonetic and 541 morphological maps (1,372 in total) containing data from 272 locations. There are a total of 14,825 linguistic variants in the data,7 i.e. each of the maps, representing an individual linguistic variable, contains on average 10.8 variants. In order to be workable for FA, these data have to be pre-processed. This is done by converting their occurrences at each location into 'weights' ranging between 0 and 1; the weight is the fraction of times a variant has been recorded at a location in relation to all records of variants at that location. Thus 0 means that a variant is not recorded at a location, 1 means that it is the only variant recorded there, 0.5 means that the variant has been recorded there together with 1 other variant, and so on, so that the values of all variants at a location add up to 1 for each variable. This seems to be the easiest and most straightforward way to deal with the non-frequency data while at the same time providing something that can be used by FA and interpreted as relative frequencies (even though as record frequencies and not necessarily as usage frequencies).

The local variant weights are filled into a location × variant matrix, which forms the basis for the analysis. Usually FA in dialectology is performed as an R-type FA, which means that spatial correlations among linguistic variants are identified. In order to perform an R-type FA, the number of cases (= sites) has to be larger than the number of items (= variants), which is clearly not the case with our data. The alternative, Q-type FA, looks for correlations among cases across items, identifying linguistic patterning of sites. The difference between Qtype FA and R-type FA is that the matrix is transposed prior to analysis, and that consequently the results are agglomerations of cases, not of items. While this is conceptually different, the outcome is very similar. "The choice of R or its transpose […] is […] not a matter of end goal but of convenience and of the ease of

6 For a complementary analysis, looking at the lexicon alone, see Pickl (2013a,b); for an integrated analysis, looking at all linguistic subsets together, see Pröll (2015).

7 The exact number of variants depends, of course, on the granularity of the classification of the records. For the SBS data, three levels of granularity have been defined, each being more general than the one before, thus aggregating more records together (cf. Pickl 2013a: 75–78; Pröll 2015: 47–48). For the analysis, I use Level 1 with the finest granularity, which means most of the differences between records are rendered as different variants.

### 5 Fuzzy dialect areas and prototype theory

meeting statistical requirements" (Cattell 1978: 326). As FA requires the number of cases to be larger than the number of items, Q-type FA has to be applied for the data used in this study to identify types of local dialects. In consequence, the factor loadings matrix contains the values specifying the relations between factors and locations. Varimax rotation is applied to optimise the results. Additionally, a factor scores matrix is calculated using Bartlett's method to specify the relations between factors and variants. Both factor loadings and factor scores can take on positive and negative values.

A further parameter to be specified is the number of factors to be extracted. This choice is much less crucial as the number of clusters for cluster analysis, since from a certain number onwards, the preceding larger factors change only very little when more factors are added. This is because each additional factor explains less variance than the ones preceding it. A popular guideline is the Kaiser criterion, which admits only factors with eigenvalues greater than or equal to 1. In this case, this means that it explains the equivalent of the variance of one location.

In the present application, the Kaiser criterion leads to a total of 16 factors. These factors account for 62.21% of the variance in the data, i.e. 62.21% of the data can be explained with recurring patterns, which is in line with Pröll's (2015) findings regarding phonetics and morphology separately. The remaining 37.79% cannot be summarised by the FA. While the number of factors may seem surprisingly high, it should be borne in mind that the number of items is also very high (14,825 variants). Even the smallest factors, with well below 1% of explained variance, still contain the same amount of variation as about a hundred variants. The amount of variance accounted for has to be seen in relation to the absolute numbers; even one of the dominant factors (Factor 11) has an explained variance of less than 1%, which illustrates that even factors this small can be indispensable for getting a complete picture.

Figure 2 shows the first factor, i.e. the factor with the highest explained variance. Each location is coloured depending on its factor loading (the darker the colour, the higher the loading). The total variance explained by this factor is 15.68%. The maximal local explained variance (which is the square of the local factor loading) is 62.41% at location 163 (Olgishofen). This factor's expanse coincides roughly with an area that is traditionally identified as the Middle East Swabian dialect area (cf. e.g. Nübling 1988: 118). As a number of variants with high scores for Factor 1 are associated with this area, Middle East Swabian can be seen as a dialect type constituted by these variants. Therefore, a variant's relevance as feature, its feature validity, is specified by its factor score from FA.

Figure 2: Factor 1 (15.68%).

Table 1 shows the 20 variants with the highest factor score for factor 1, or the top 20 of the features of Middle East Swabian. Even though these are only the top 20 out of 2,557 variants with positive factor scores (most of them have scores close to zero), some linguistic phenomena can be ascribed to this factor: the loss of *h*, *ch* and *g* in certain positions (5, 8, 9, 14, 17, 19), the realisation of MHG *ou* as *ao* (3, 4, 7, 10, 11), and the preservation of vowel length (8, 12, 14). A deeper look at the variants with high factor scores can lead to additional insights in the linguistic make-up of this dialect type and in the alignment between variants and their distributions, but is skipped here for reasons of brevity.

Figures 3–10 show the geographic distributions of all the other dominant factors (Factors 2–7, 10–11), i.e. of all the factors that are strongest at one location at least. The explained variances are given in brackets. Divergent colours in the individual maps represent negative loadings.

Most of these factors can be associated with traditional dialect areas: Factor 2 with North East Swabian, Factor 3 with East Algovian, Factor 4 with Central Bavarian, Factor 5 with Lechrainian, Factor 6 with Northern Bavarian, Factor 7

Table 1: Top 20 features of Factor 1. MHG: Middle High German. OHG: Old High German.

Simon Pickl

(a) Figure 3a: Factor 2 (14.04%). (b) Figure 3b: Factor 3 (8.98%). (c) Figure 3c: Factor 4 (5.74%).

(d) Figure 3d: Factor 5 (4.68%). (e) Figure 3e: Factor 6 (3.88%). (f) Figure 3f: Factor 7 (1.91%).

### 5 Fuzzy dialect areas and prototype theory

(a) Figure 4a: Factor 10 (1.02%). (b) Figure 4b: Factor 11 (0.85%). (c) Figure 4c: Combined factor map.

with West Algovian, Factor 10 with East Franconian. Factor 11 appears to capture variants that are characteristic for towns and cities: The agglomeration around location 122 is the metropolitan area of Augsburg, and most of the other locations with high loadings are larger towns: Landsberg am Lech (199), Memmingen (205), Kaufbeuren (240), Weilheim (Oberbayern) (259), Neu-Ulm (109), Günzburg (96), Dillingen (70), Kaisheim (38), Rain am Lech (53), Donauwörth (49), Nördlingen (17), Oettingen (10), Monheim (30) with its boroghs Itzing (29) and Weilheim (21), Wemding (20), Möhren (13) (borough of Treuchtlingen). The correlation between Factor 11's loadings and the populations8 of all 272 locations is 0.45, which corresponds to an explained variance of *R* 2 = 20%; the logarithmic relation is somewhat stronger (*R* 2 = 28%). Factor 11, therefore, can be interpreted as a geographically discontinuous urban variety; it captures variants that are used predominantly and typically in larger towns and cities. Table 2 lists the top 20 features for this type. The preservation of vowel shortness (2, 3, 4, 5, 6, 8, 12, 15, 16, 17, 18, 20) seems to be especially characteristic of this factor. It does not come as a surprise that almost all of the variants are identical with the respective standard variants. The lenition of plosives and fricatives (1, 10, 19, 20) seems to be an exception (except for 10, where lenition occurs also in the standard), which would qualify it as a unique feature of regional urbanity that is distinct from the standard.

8 Figures for 1971 are taken from: Bayerisches Statistisches Landesamt (1972).

Table 2: Top 20 features of Factor 11.

### 5 Fuzzy dialect areas and prototype theory

In Figure 11, all dominant factors are combined into one map, with each location assigned to the locally dominant factor. Consequently, only information about the locally dominant factors is depicted, which means that only the surface of the dialectal landscape is visible. The resulting division into areas is in principle comparable to classifications obtained using cluster analysis or similar methods. A distinction of the present map lies in the colour shades of the individual locations, which represent the different degrees of dialect area membership. Another benefit of these results is that they retain variation 'below' the threshold of dominance, which is not visible in Figure 11 but latently present. This variation belongs firstly to the locally non-dominant parts of the globally dominant factors: each factor has loadings other than zero outside of its dominance area, but these proportions are hidden. However, they can be viewed by regarding one factor at a time (Figures 2–10).

There are also factors that are dominant nowhere in the area under investigation. They do not show up in Figure 11 at all, but again, they are latently present and can be viewed individually (Figures 12–18). Summarising small fractions of the data, they contribute to a more complete picture of overall variation and the dialectal landscape, even though they represent non-dominant dialect types, dialect areas without a core area. Many of the factors can be interpreted in a meaningful way. Several of the factors shown in Figures 12–18 seem to be related to (former) market towns: their central areas (and in some cases also their counter-centres with negative values, in red) coincide with the respective market towns' catchment areas (as documented in Volume 1 of the SBS). For Factor 12, the blue centre correlates with the catchment area of (Neu-)Ulm (109), the red centre with the catchment area of Mindelheim (195); for Factor 13, the blue centre correlates with the catchment area of Lauingen (without number); for Factor 14, the blue centre correlates with the catchment area of Nördlingen (17), the red centre with that of Wertingen (72); for Factor 15, the blue centre correlates with the catchment area of Jettingen (without number), the red centre with the catchment area of Memmingen (205), for Factor 16, the blue centre correlates with the catchment areas of Schongau and Weilheim (Oberbayern) (259), the red centre with the catchment area of Mering (158).9 These effects are relatively weak – the factors have between 0.51% and 0.85% of explained variance, which is, however, still the equivalent of 76 to 126 variants and their distributions, and they are clearly associated with their respective counterparts. Hence it is justified to speak of non-dominant dialect types that are constituted by features character-

9 For similar findings for lexically-based factors and a more in-depth discussion, see Pickl (2013a: 170–196); Pickl (2014).

### Simon Pickl

istic of these towns' surrounding areas. With these findings, a level of detail and depth is reached that goes beyond what has been attainable with previous methods of dialect classification.

(0.62%).

(0.60%).

(0.71%).

### 5 Fuzzy dialect areas and prototype theory

(0.51%).

### **4 Conclusion**

It has been demonstrated how prototype theory can be used not only to describe emic, folk ideas of dialects, but also to establish a scholarly, etic notion of dialect areas. Since the two are conceptually similar, they can be compared in a straightforward way in the future to gain insights in the relative importance of individual variants and their evaluation and assessment, e.g. based on their salience.

In this paper, it was argued that emic and etic dialect areas alike are best viewed as fuzzy dialect types, which can be described in terms of prototype theory. Dialect types have an unsharp spatial expanse, individual locations exhibiting differential membership values, and are characterised by linguistic features that have individual degrees of relevance for a type.

Following this approach, dialect areas or types are constituted by sets of cooccurring features. It was argued that factor analysis, which has been used before in dialectology, is a suitable method for the identification of such sets and thus of dialect types. Its expedience was demonstrated using data from the *Sprachatlas von Bayerisch-Schwaben* (SBS), yielding 16 factors representing dialect types. Nine of them are locally dominant within the area of investigation and lead to a classification into fuzzy dialect areas, with broad spans of overlap. Seven are non-dominant, because everywhere other factors are stronger; they, too, represent meaningful patterns and can be interpreted, as was illustrated using cities' and towns' catchment areas.

### **Acknowledgments**

This work has been supported by the *Deutsche Forschungsgemeinschaft* (DFG), which funded the joint research project *Neue Dialektometrie mit Methoden der stochastischen Bildanalyse* of the Department of German Linguistics (University of Augsburg) and the Institute of Stochastics (Ulm University). My cordial thanks go to all of my colleagues who have contributed to this work, as well as to the editors and referees of this volume.

### **References**

### **Chapter 6**

## **On the problem of field worker isoglosses**

### Andrea Mathussek

University of Freiburg

As a number of authors have shown, the traditional approach of collecting and transcribing speech material in direct investigations may lead to differences in the data that do not represent real diatopic variation but are based on the individual transcription habits of the different field workers involved. If such differences result in isoglosses separating not speech areas but different field workers' investigation areas ("field worker isoglosses"), researchers run the risk of mapping the organizational structure of projects instead of true linguistic similarities or differences. The following article shows how dialectometric analysis can be used to visualize field worker phenomena in speech data using the example of the *Sprachatlas von Mittelfranken* (SMF), which is part of the *Bavarian Speech Atlas*.

### **1 Definition: field worker isogloss (FWI)**

To define the concept of 'field worker isogloss' (FWI) one has to start by explaining the term "isogloss". Isoglosses are usually characterized as boundary lines between two dialect realizations of a linguistic phenomenon (cf. Glück 2005: 296– 297). An isogloss – or sometimes a bundle of more than one isogloss – then forms the border of a speech area in traditional dialect geography. The phenomenon is illustrated in Figure 1. Here, the different colors of the speech bubbles symbolize the different realizations of the linguistic variable under investigation and the blue line stands for the isogloss.

Based on this concept, FWIs can be defined as boundary lines between two different realizations in phonetic transcription, given that there is no variation in the audio data under investigation. Field worker isoglosses are thus boundary lines between two field worker areas. The illustration on the right in Figure 1 exemplifies this.

A (fictitious) example might be the realization of the vowel in *Frosch* ('frog'), which may be pronounced [u:] by the informants from both villages. This is

Andrea Mathussek. 2016. On the problem of field worker isoglosses. In Marie-Hélène Côté, Remco Knooihuizen & John Nerbonne (eds.), *The future of dialects*, 99–116. Berlin: Language Science Press. DOI:10.17169/langsci.b81.145

Andrea Mathussek

Figure 1: Schematic layout of the concepts "isogloss" and "fieldworker isogloss"

symbolized by the usage of red color for both speech bubbles. The particularity now lies in the fact that despite the consistency of the informants' speech the field workers write down two different phonetic symbols and thus differ in their phonetic transcription of the speech, which is illustrated by the use of different colors for the note pads.1

It might be expected that field worker phenomena only occur rarely – especially because the problem has been well known in dialectology for quite a while (cf. Hotzenköcherle 1962: 59–73). Unfortunately, it appears that the problem occurs more often than expected.

In this paper I will explain – based on the example of the *Middle Franconian Speech Atlas* (SMF) – how field worker effects find their way into linguistic data and analysis. While there are a number of established practices used by project members to avoid this problem, even atlases published in the last 10 years betray traces of the individual field workers' influence. I will then show how dialectometry can be useful to discover fieldworker phenomena in the transcribed corpora of linguistic data surveyed by different people in different places.

### **2 The potential risk in traditional dialect atlas data**

### **2.1 Example data**

The data serving as an example in this article comes from the *Sprachatlas von Mittelfranken* (Dialect Atlas of Middle Franconia, SMF). This project started in 1989 and was supervised by Horst Haider Munske in Erlangen (cf. Munske 2013: 11). The map in Figure 2 shows the position of the administrative district Middle Franconia in Germany marked in red. It is located in Southern Germany and adjoins Bavaria in the west. The region is interesting from a dialectological point of view

1 Phonetic transcription is always only a model for reality. Versions A and B may also differ in their degree of detail. This may lead e.g. to one field worker marking nasalization or lenition while another leaves them unmarked. Differing transcriptions do not always mean that somebody made a mistake (cf. Mathussek 2014: 41–69).

### 6 On the problem of field worker isoglosses

because three High German dialects meet here: Eastern Franconian, Swabian and Northern Bavarian (cf. SMF 1: 7).

Figure 2: Position of Middle Franconia in Germany (GFDL. Orginal source: http://commons.wikimedia.org/wiki/File:Locator\_map\_Mittelfranken\_ in\_Germany.svg)

It is worth emphasis that the *Middle Franconian Atlas* is only one example where such phenomena occur — other atlases using similar traditional methods may be expected to reflect transcriber-specific phenomena as well.

### **2.2 Reasons for FWIs**

As Kerswill and Wright summarize: "Transcription is a messy thing" (Kerswill & Wright 1990: 273). I will illustrate this with reference to the SMF in the following pages.

*The Middle Franconian Atlas* – like most of the regional dialect atlases of the 20th century – is an atlas of the so-called 2nd generation. That means that direct investigation was used as a method to collect data.2 Typically, atlases of this type are mono-dimensional atlases3 with a few exceptions. The interviews took place in the informants' living rooms and were usually recorded on cassettes. However, the audio material was used as a reference source in problematic cases

2 For the direct method, a "trained fieldworker makes on-location recordings and works through a comprehensive questionnaire with the informant. […] The investigator and the informant are united in the attempt to unearth the oldest accessible form at a particular location […]." (König 2010: 502)

3 Mono-dimensional atlases, in contrast to two-dimensional atlases, do not take pragmatic or social variation into account. (cf. *Mittelrheinischer Sprachatlas* Institut für Geschichtliche Landeskunde an der Universität Mainz (ed.): *Mittelrheinischer Sprachatlas*; http://www.igl.unimainz.de/service/impressum.html, 2015/05/20)

### Andrea Mathussek

Figure 3: Original page of a SMF-questionnaire

rather than as the basis for analysis. Instead, the basis for analysis consisted of the questionnaire and the handwritten phonetic transcriptions the field worker noted immediately after the informant answered a question.

Figure 3 shows a part of one page from the Middle Franconian questionnaire. The "questions" are printed in the left column. They are sometimes real questions as in number 6 "what do you call it when a cow does not give any milk for some time before she calves?" The answer here can be translated literally as "stands dry" and is written down in Theutonista in the right column by the field worker. This happens "on line", while the informant is waiting for the next question. It is evident that such time pressure can lead to imprecision or even mistakes. This becomes even clearer when we have a closer look at the transcription system here.

Figure 4 shows part of the vowel diagram as it was used for the Bavarian Speech atlases. The enlarged part lists some of the possibilities to record open

Figure 4: Part of the Theutonista vowel diagram (cf. SMF 1: 82)

fronted vowels. It becomes obvious that it is nearly impossible for two persons to write down the same symbol with exactly the same diacritics when this degree of detail is used. This is at least one source of possible interpersonal differences in the transcriptions.

Such interpersonal variation, of course, becomes a problem, especially when more than one person is working in the field.4 But a division of labor is often necessary, even in comparatively small areas of investigation such as the administrative district of Middle Franconia, if a high density of locations is to be surveyed.

For the *Atlas of Middle Franconia*, investigations were carried out in 167 mainly rural villages in an area of 7.000 square kilometers. In every village, about six people (men and women) served as informants – which makes a total of around 1.000

4 Even with one transcriber, there may be intrapersonal inconsistencies.

### Andrea Mathussek

interviews with an average duration of 10 hours. The questionnaire contained 2.808 questions (cf. SMF 1: 29 ff.). It is quite clear that it would be impossible for one person to manage 10.000 hours of interviews. The easiest and most economical way to split the work was to divide the area of investigation into areas assigned to different field workers. The field workers at the *Middle Franconian Atlas*, for example, lived in different areas of the administrative district or had family there – it was therefore an obvious solution to divide the area according to the field workers' locations.

### **2.3 Arrangements to avoid FWIs**

It is interesting to see what measures the project members undertook to avoid the appearance of field worker phenomena. First of all, the members of the project were aware of the fact that such effects can occur in the data, as previous and neighboring projects had also alluded to the problem and suggested solutions.5 In the introductory volume, the text near the map showing the field workers' areas (see Figure 5) says: "Map 11 shows which investigations were carried out by which field worker. The field workers' individual background and the specifics of their phonetic transcriptions are given below" (Klepsch 2013a: 47). The authors even identify and describe individual transcribing habits,6 which is an indication of transparency. A closer look at the description of those individual "specifics of phonetic transcriptions" as they are described in the introductory volume will make this point even clearer. The following text is a translation of Alfred Klepsch's characterization as a field worker:

Alfred Klepsch was born in Schwabach in 1954 and lived there from 1954 to 1961. From 1961 to 1974 he lived in Spalt, from 1974 to 1986 in Schwabach again, from 1986 to 1995 in Baiersdorf, from 1995 to 2000 in Erlangen and since then he has been living in Nuremberg. He speaks the regional dialect of the Nuremberg area. […] His transcription shows indetermination especially in the area of half-closed short vowels. From 1989 to 1992, he nearly always recorded open e-sounds [corresponds closest to æ in IPA] and osounds. After the coordinator M. Renn told Klepsch that the dialect variant of the Middle High German primary umlaut e has a more closed quality […] the field worker tried to close that "hearing gap". The transcriptions of 1993, then, certainly contain some hypercorrections […]

(Klepsch 2013a: 47 [translated and slightly adapted])

5 Cf. e. g. Hotzenköcherle (1962: 59) and König (1997: 45).

6 The SDS again serves as a model here (cf. Hotzenköcherle 1962: 61–73).

Figure 5: The field workers' working sections in the SMF project (SMF 1: 48)

### Andrea Mathussek

The text deals with the background and the individual transcribing habits of Alfred Klepsch, who was the coordinator of the project. In the first paragraph it provides the reader with information about the villages in Middle Franconia Alfred Klepsch lived in and information on his speech and transcribing habits. Further on, the text honestly names a number of Klepsch's habits, which shows a really high degree of transparency. The question is: How often does a reader not only notice such texts in introductory volumes, but also use them for his or her interpretation of the data or the maps?

The members of the project did not leave it up to the reader to work out the problem of FWI, but rather applied various methods to address this issue.

First of all, there was a lot of discussion and comparison among the sister projects (cf. Klepsch 2013b: 25 ff.). The Middle Franconian Atlas is only one part of a big project comprising the whole federal state of Bavaria. Moreover, the field workers within the project always made great efforts to adjust their transcriptions. They used methods such as co-transcribing and attending each other's test investigations, while also organizing meetings and workshops. In addition there was one interview which was transcribed by all field workers. This transcription served as a reference point from then on (cf. Klepsch 2013b: 26).

Klepsch writes about the beginnings of the project in retrospective: "The comparison of the transcripts and the audio data completely puzzled the field workers. The differences between the […] transcripts were immense" (Klepsch 2013b: 25). Despite all the efforts he concludes: "None of the field workers achieved a level of perfection in the course of his or her career" (Klepsch 2013b: 27).7

### **3 Dialectometry as a means to discover FWI**

Field worker effects are often considered something you have to expect in the data – but also something that can be addressed and dealt with when it comes to interpretation.8 Because of this common opinion field worker isoglosses were not really on my mind when I was attempting to detect speech areas and speech

7 As Kerswill and Wright (1990: 226) point out, a reason for this may be that transcribers use different strategies for "rationalizing and reducing to symbols the differences they have heard" (cf. Kerswill & Wright 1990: 269).

8 Ogura and Wang proposed a statistical "method for clarifying fieldworker isoglosses" using the Spearman rank order correlation. They correlate the frequencies of reflexes of different ME vowels inside and outside individual investigation areas of the SED. This method sounds very promising to me. For reasons of simplicity with respect to implementing the method, I will describe another procedure to detect field worker phenomena (cf. Ogura & Wang 1992).

6 On the problem of field worker isoglosses

borders in the investigation area of Middle Franconia – using the data and material of the SMF-project (cf. Mathussek 2014).

The dialectometric analysis of the realization types of 517 lexemes/phrases showed that the individual variants formed cohesive areas, irrespective of the specific statistical method used. The colored regions in Figure 8 (not the symbols) are the result of a clustering technique (Ward's method, 8 clusters) carried out by Gabmap.9 Figure 6 shows a small part of the table that was used as a basis for the analysis with Gabmap.

Figure 6: Part of the data table prepared for the analysis with Gabmap ('cow', 'straw', 'height', 'the old houses'; locations 1 to 12). The table does not show IPA or Theutonista but the so called "Kodate" – a code that translates the basic signs and diacritics of Theutonista into ASCII (American Standard Code for Information Interchange) (cf. Reichel 2013: 38–40).

The comparison of this analysis with maps developed with traditional methods then revealed some discrepancies. Figure 7 visualizes some results of the traditional approach. I picked out about 40 Middle High German speech sounds in different sound environments and looked for different realizations in the 167 villages. The resulting isoglosses in the area of consonants are shown in black; the isoglosses for vowel phenomena are marked in red.

What attracts one's attention here is that besides some obvious similarities between the isoglosses in Figure 7 and the areas in Figure 8 – such as the bundle

9 Gabmap is a free "web application that visualizes dialect variation", which was developed in Groningen. (cf. Nerbonne et al. 2011.) For a description of how cluster analysis works see http://www.gabmap.nl/~app/doc/manual/clustering.html, accessed 2015/5/21.

Andrea Mathussek

Figure 7: Traditional approach: consonantal and vocalic isoglosses (Mathussek 2014: 107).

of isoglosses along the western border of the area of investigation and the area in light orange and pink taken together – there are areas on the map in Figure 8 that do not coincide with the results of the traditional analysis.

The explanation for the apparently new borders problem is revealed when one considers the striking match between the dialectometric analysis and the investigation areas of the individual field workers. What the dialectometric analyses showed were, without much doubt, not really speech areas, but rather field worker effects in the data!

The map in Figure 8 is a blend of a Gabmap cluster map and the map in the introductory volume of the Middle Franconian Atlas showing which field worker collected data in which village (SMF 1: 48, see Figure 5 above). There is a noticeable coincidence of symbols and clusters on the map. In particular, the dark green and the light blue section match the symbols one-to-one, which means that exactly one field worker worked in all the villages Gabmap clustered together here as one. It was also exactly one person who was responsible for both the light green and the light orange areas, taken together.

The next task was to find out which properties of the data were responsible for the clustering – or in other words: whether different transcription habits were so

### 6 On the problem of field worker isoglosses

Figure 8: Clusters and field worker's collection areas (Mathussek 2014: 216).

influential that they marginalized the real linguistic differences. Gabmap offers a very useful tool named cluster determinants. The tool helps to identify those lexemes that are the most relevant for a cluster. That means the realization types are very similar or identical within the cluster and they rarely or never appear in other clusters.10

I will only give a few examples here, but more are to be found. The analysis of the cluster determinants for the light green cluster (cf. figure 8) showed that in the villages in this area, words like [o:ʃdən]11 *Ostern* ('Easter') and [dsɪxəd] (*er*) *zöge* ('he would pull') often have the highest values in the cluster determinants' analysis. Moreover, this field worker noted aspirated voiceless dental plosives, whereas the others didn't. One example here is [gvidəth ] *gefüttert* ('fed'). Thirdly, the field worker in this area transcribed clusters of vowels and used many diacritics, as in *heiraten* ('marry'), which may look like Figure 9 in Theutonista. This was a peculiarity that very rarely occurred in other regions.

In case of the light blue cluster in the south of Middle Franconia we find a very clear example of a field worker phenomenon. Figure Figure 10 shows the

10 Cf. Nerbonne et al. (2011).

11 IPA is used here to make the presentation more generally accessible. As I pointed out above, SMF and the other Bavarian speech atlases used Theutonista.

Andrea Mathussek

Figure 9: Theutonista transcription of *heiraten* ('marry').

Figure 10: Distribution of <w> (Mathussek 2014: 226).

distribution of the phonetic symbol <w>, which stands for the bilabial fricative in Theutonista. As one can see, the area corresponds perfectly with the cluster in light blue in the south of the investigation area (cf. Figure 7). A closer look into the data shows that <w> only appears in transcripts from this area where two neighboring projects made investigations. The *Middle Franconian Atlas* got data here from the *Atlas of Bavarian Swabia* (SBS) and they had slightly different transcription conventions there (cf. SMF 1: 30). In the rest of the investigation area the field workers used the sign <ß> in all of the cases where the Swabians used <w>. This shows a very clear case of field worker influence indirectly reflecting phenomena in a neighboring project. However, those cases are not the dangerous ones because they can easily be recognized in the data:12

Figure 10 is an example for a distribution map made with Gabmap. This feature allows the mapping of the distribution of individual items or strings of items in the data. In this case the map shows that the symbol <w> only occurs in those locations that were examined by members of the SBS project (light blue cluster in Figure 8).

12 At least when the editor takes a closer look into the data.

All in all, the data analysis showed a clear correlation between the Gabmap clusters and the field workers' individual areas of investigation. In the corpus, individual transcription habits were much more relevant for initial analysis than the real linguistic differences.

The last step was then to check the linguistic features which the cluster determinants analysis had shown were especially relevant for the structure of the data in the audio material. Of course, only a random sample could be tested here. The check confirmed the results of the dialectometric analysis: in most cases under suspicion, the differences in the transcriptions were not a matter of real linguistic variance.

To sum up, I recommend a dialectometric analysis of dialect geographical data in six steps:13 Firstly, the whole corpus is mapped using a cluster technique with approximately the number of clusters that would be expected from traditional approaches. Secondly, the results should be compared (if possible) to the results of traditional approaches. In a third step, the results should be compared to the information about investigation areas and field workers that is accessible. If there are inconsistencies, or if the areas seem to correspond more to the investigation areas than to the areas noted in traditional approaches (or if there is no accessible information on one of the aspects) the analysis of cluster determinants should be conducted to find out about the lexemes or phrases that are mostly "responsible" for the clusters in a fourth step. The results of this analysis can be checked with distribution maps (fifth step), before in a sixth step relevant features should be examined in the audio data.

### **4 FWIs on actual maps in the printed atlases?**

Subsequently, an important question to ask was to what extent this had influenced the maps and results in the printed volumes of the atlases. The following two short examples show that field worker phenomena even made it into the printed volumes.14

The first example can be found in map 45 in volume 4 of the *Middle Franconian Atlas*. It deals with the realizations of Middle High German t in the position

13 Of course, this analysis can be done with different software, too.

14 It's easier to discover field worker phenomena in the volumes of the SBS, where the members decided to print investigation areas on every base map. This enables a quick check whether feature isoglosses correlate or not with boundaries of fieldworker areas (cf. SBS 1: 45). Due to the fact that 13 people worked as field workers for the SMF (SMF 1: 48) project (but only 3 for SBS, SBS 1: 45), the investigation areas are not printed on the base map in the Middle Franconian Atlas.

Andrea Mathussek

Figure 11: Field worker effects in SMF 1, map 45: MHG t in the position between -s- and -en and one individual field worker's working area (in green) (cf. Mathussek 2014: 240).

between -s- and -en as in the lexemes *Bürste* ('brush'), *Fenster* ('window'), *Gerste* ('barley') and *Husten* ('cough'). The green line around the investigation area of the field worker Johannes Bauer in Figure 11 corresponds perfectly to the area where reduced plosives were plotted on the map. Nowhere else in the area of investigation had field workers noted reduced plosives. The unique shape of the area makes it hard to believe that the coincidence is only accidental.

The second example (see Figure 12) is taken from the second volume of the *Middle Franconian Atlas* and refers to the realizations of the vowel in the demonstrative pronoun *die* (meaning 'this' or 'these'). This part of map 100 is intended to illustrate the border between a realization type "closed vowel" and a realization type "neutral or open vowel". I drew an isogloss between the two types and compared it to the border between Alfred Klepsch's (orange) and Gunter Schunk's (light green) investigation area. There is a one-to-one match between the two borders – despite the fact that its course is quite unique.

Those are only two examples, but it is quite certain that there are more – and not only in the *Middle Franconian Atlas*. In both cases mentioned above, audio 6 On the problem of field worker isoglosses

Figure 12: Field worker effects in SMF 2, map 100: Realization of the vowel in the demonstrative pronoun *die* (Mathussek 2014: 241).

data was checked for reference. It was not possible to identify any differences between the dialect realizations inside and outside the clusters.

Field worker isoglosses, then, are present in speech atlases and are presented as real isoglosses separating speech variants.

### **5 Implications**

The findings of this analysis lead to a few implications.

First of all, it is important for anyone working with speech atlases in which different field workers were responsible for collecting the data to pay attention to the field workers' individual areas of investigation. That, of course, may not always be possible or easy because the degree of transparency varies a lot from atlas to atlas.

Despite that fact, the search for field worker phenomena has to be carried out systematically and needs to be expanded to other atlas projects and their maps and data. Dialectometric approaches can be used to explore the data and to identify the relevant features in the data.

Furthermore, the findings show that the analysis of large amounts of data does not make traditional approaches redundant. That does not mean that dialect geographers have to lean over hand-drawn maps again – Gabmap offers tools for "traditional" methods, too – but a close examination of the data is absolutely necessary.

A last point concerns the question of the degree of detail in phonetic transcription. Do we really want all those diacritics if it is seemingly impossible for two people to use them in the same way?

### Andrea Mathussek

Here, more modern approaches and methods of investigation seem to provide help. For the project "Effects of the national border on the linguistic situation in the Upper Rhine Area" (*Frontière linguistique au Rhin Supérieur*, FLARS) for example, there were no transcripts made in the actual investigation situation, but the researchers only transcribed relevant parts later with the help of the audio data (cf. Auer et al. 2015).15 This leads to a closer relation between audio data and analysis; the process of (phonetic) transcribing is carried out by one person16 in a relatively short time, and the focus is on the aspect that will be the object of analysis.

### **References**

Glück, Helmut. 2005. *Metzler Lexikon Sprache*. Stuttgart; Weimar: Metzler.

Hotzenköcherle, Rudolf. 1962. *Einführung in den Sprachatlas der Deutschen Schweiz. A: Zur Methodologie der Kleinraumatlanten*. Bern: Franke.

König, Werner. 1997. *Sprachatlas von Bayerisch-Schwaben. Band 1: Einführung*. Heidelberg: Winter.

15 Cf. also the note on Hammarström's method of *transcription phonétique indirecte* ('subsequent transcription of audio material under laboratory conditions') in Hotzenköcherle (1962: 73).

16 This method, of course, does not solve the problem of "within-transcriber variability" (Kerswill & Wright 1990: 258).

### **Chapter 7**

## **Tracking linguistic features underlying lexical variation patterns: A case study on Tuscan dialects**

### Simonetta Montemagni

Istituto di Linguistica Computazionale "Antonio Zampolli", ILC-CNR

### Martijn Wieling

University of Groningen, CLCG

In this paper, we illustrate the application of hierarchical spectral partitioning of bipartite graphs in the study of lexical variation in Tuscany based on the data from a regional linguistic atlas. This method makes it possible not only to identify existing patterns of lexical variation in Tuscany, but also to uncover the underlying lexical features in terms of the most characteristic concept-lexicalization pairs. The results are promising, demonstrating the potential of the method for tracking the linguistic features underlying identified patterns of lexical variation and change across generations.

### **1 Introduction**

In dialectometry (Séguy 1971) the focus lies on the aggregate analysis of dialect variation. In contrast to "cherry-picking" a few linguistic items confirming the analysis one wishes to settle on (Nerbonne 2009), the advantage of the aggregate approach is that it offers a more objective view of dialect variation. Unfortunately, many studies focusing on the aggregate pattern of dialect variation have disregarded the underlying linguistic basis. As a consequence, linguists have remained critical of the dialectometric approach (Schneider 1988; Woolhiser 2005; Loporcaro 2009).

To counter this criticism, various new dialectometric methods have been developed aimed at identifying the linguistic basis of dialectal variation (as reviewed in Wieling & Nerbonne 2015). For example, Nerbonne (2006) and Pröll, Pickl

Simonetta Montemagni & Martijn Wieling. 2016. Tracking linguistic features underlying lexical variation patterns: A case study on Tuscan dialects. In Marie-Hélène Côté, Remco Knooihuizen & John Nerbonne (eds.), *The future of dialects*, 117–135. Berlin: Language Science Press. DOI:10.17169/langsci.b81.146

### Simonetta Montemagni & Martijn Wieling

& Spettl (in press) use an approach based on factor analysis, whereas Shackleton (2005) uses principal component analysis. Grieve, Speelman & Geeraerts (2011) follow the workflow of traditional dialectology (i.e. identifying isoglosses, bundling isoglosses and cluster analysis) by using multivariate spatial analysis.

The method we will apply here, Hierarchical Bipartite Spectral Graph Partitioning (HBSGP), has been developed by Wieling & Nerbonne (2009; 2010; 2011), who adopted it from information retrieval (Dhillon 2001) and applied it to dialectology. HBSGP results in a clustering of geographical varieties while *simultaneously* providing a linguistic basis for each of the identified clusters. The approach of Wieling & Nerbonne (2011) has been successfully applied to study phonetic variation in Dutch dialects (Wieling & Nerbonne 2011), English dialects (Wieling, Shackleton & Nerbonne 2013) and Tuscan dialects (Montemagni et al. 2012; 2013). More recently, the method has also been applied to investigate lexical variation in contemporary English dialects on the basis of the BBC *Voices* data (Wieling et al. 2014).

In this study, we focus on lexical variation. Our dataset, a regional lexical atlas of Tuscan dialects whose data have a diatopic and diachronic characterization, allows us to explore the potential of the HBSGP method in the study of lexical variation. In particular, it enables us to identify lexical features and their relationships on the one hand and to reconstruct the dynamics of lexical change across generations on the other hand. Technically, a new measure is proposed for determining the most important lexical features associated with the identified dialectal areas.

### **2 Data**

We investigate Tuscan lexical variation on the basis of a linguistic atlas of Tuscany, the *Atlante Lessicale Toscano* (ALT, Giacomelli et al. 2000), now available as an online resource (http://serverdbt.ilc.cnr.it/ALTWEB). ALT is a regional Italian lexical atlas focusing on dialectal variation throughout Tuscany, where both Tuscan and non-Tuscan dialects are spoken. In this paper we focus on Tuscan dialects only, recorded in 213 localities by a total of 2060 informants who were selected with respect to various socio-demographic parameters (such as age, education and gender).

ALT interviews were carried out on the basis of a questionnaire of 745 target items, designed to elicit mainly lexical, but also semantic and phonetic variation. This study is based on the results of onomasiological questions, i.e. starting from concepts and looking for their lexicalizations. A typical onomasiological ques-

### 7 Tracking linguistic features underlying lexical variation patterns

tion asks how a given concept is designated or named, e.g. "what is the name for flat and crispy bread, seasoned with salt and oil?". To avoid interference with non-lexicalized answers, we excluded questions prompting 50 or more distinct lexical items. Furthermore, we only considered nouns (the large majority of items of ALT questionnaire) in this study. The resulting subset consists of 170 questionnaire items for which a total of 5,174 distinct normalized answers were given (on average 30 lexical variants per concept) distributed into 61,496 geo-referenced responses (i.e. associated with locations). The total number of speaker-responses was 384,454.

To abstract away from phonetic variation, we used the most abstract representation level present in ALT (Cucurullo et al. 2006). This normalized representation was meant to abstract from phonetic variation (caused by productive phonetic processes), but did not remove morphological variation or variation caused by unproductive phonetic processes. In this study we used the normalized lexical answers to the selected subset of 170 onomasiological questions. The same set of questions has also been used by Wieling, Upton & Thompson (2014) in a study of lexical differences between Tuscan dialects and standard Italian.

The representativeness of the selected sample with respect to the whole set of ALT onomasiological questions (i.e. a total of 460 questionnaire items) was assayed using the correlation between overall lexical distances and lexical distances obtained from the selected sample (Wieling, Upton & Thompson 2014). The Pearson's correlation coefficient was *r* = 0.94, showing the representativeness of the selected sample with respect to the whole set of onomasiological questions.

### **3 Methods**

In this study, we use hierarchical bipartite spectral graph partitioning as our method of choice (Wieling & Nerbonne (2011)). As mentioned before, this approach simultaneously clusters the geographic locations together with the linguistic features characterizing them. In this case, a cluster of locations is characterized by a linguistic basis expressed in terms of the most salient lexical features. These lexical features can be seen as a proxy of the traditional notion of lexical isoglosses, establishing the boundaries of dialectal areas.

Every variety attested in a given location is described in terms of Concept-Lexicalization (CL) pairs linking each of the 170 selected concepts with its lexicalization(s) (reported in the normalized form) in the specific location. CL frequencies are normalized by dividing the number of recorded answers by the number of informants in a given location, with their value ranging between 0 and 1. Since

### Simonetta Montemagni & Martijn Wieling

there was a socio-demographically differentiated group of informants potentially giving rise to multiple responses to denote the same concept for each location, the sum of normalized frequencies of lexical variants associated with the same concept in a certain location can be greater than 1.

The input for the HBSGP method is a bipartite graph which contains two sets of vertices, locations and CL pairs, connected by lines. There exists a line between a location and a CL pair whenever at least one of the speakers in the location uses the lexical variant. The lines are weighted between 0 and 1. A value of 0 indicates that no speakers in the location use the lexical variant (and thus equals the absence of a line), whereas a value of 1 indicates that all speakers in the location use the lexical variant to denote the concept being investigated. Table 1 gives an example of (a tabular representation of) the bipartite graph, with the rows corresponding to the locations and the columns to the CL pairs. About 80% of the speakers in Caprese Michelangelo use the form *aràncio* to denote an orange (henceforth, concept denominations are represented by small caps). A similar number of speakers also uses *melàngola* to denote the same (speakers frequently provided multiple lexicalizations to denote a certain concept).

The input matrix is then subjected to Singular Value Decomposition (SVD), and the *k*-means clustering algorithm (with *k* equals 2) is applied to the results of the SVD resulting in a two-way clustering. The *k*-means clustering was repeated 1000 times for robustness. As the output of the SVD combines the locations with the CL pairs, the clustering likewise groups locations and CL pairs. Consequently, lexical variants grouped with locations can be seen as characteristic elements of those locations. For more mathematical details, we refer the interested reader to Wieling & Nerbonne (2011).

In order to identify the most characteristic linguistic features for a group of locations, Wieling & Nerbonne (2011) combined two different criteria which were implemented in two different and complementary measures: representativeness and distinctiveness. Representativeness measures the relative frequency of the lexicalization of a given concept in the locations in the cluster. For example, if the cluster contains ten locations and all speakers in seven locations use the lexical variant, the representativeness is 0.7. Distinctiveness measures how frequently the lexical variant occurs within as opposed to outside of the cluster (corrected for the relative size of the cluster, which is calculated by dividing the number of locations in the cluster by the total number of locations in the dataset). A distinctiveness of 1 indicates that the lexical variant is only used inside the cluster. The distinctiveness equals 0 when the relative frequency of the lexical variant in the cluster is equal to the relative size of the cluster (i.e. it is not distinctive). Inter-

### 7 Tracking linguistic features underlying lexical variation patterns

Table 1: Tabular representation of a bipartite graph. The numbers represent the normalized frequency (obtained by dividing by the number of speakers) of the lexical variant associated with a given concept in the different locations which ranges between 0 and 1. As the speakers may use multiple variants to denote a concept, the normalized frequencies associated with a concept in a certain location do not have to sum to 1.

estingly, the measures of representativeness and distinctiveness are reminiscent of the "consistency" and "homogeneity" measures introduced by Labov and colleagues for the construction of isoglosses in the *Atlas of North American English* (Labov, Ash & Boberg 2006). Homogeneity measures how much variation exists within the region defined by the isogloss (i.e. corresponding to a non-chance corrected variant of distinctiveness) and consistency (i.e. corresponding to representativeness) measures how strongly the variable is concentrated within a given region.

The two measures capture two different equally important desiderata of isoglosses: to put it in the words of Labov, Ash & Boberg (2006), "First, we want the area defined to be as uniform as possible […]. Second, we want as high a proportion of hits as possible to be located within the isogloss". For this reason they need to be combined. Wieling & Nerbonne (2011) combined representativeness and distinctiveness measures by averaging them, yielding the importance score. Here, we propose that to determine the relevance of CL pairs in the characterization of identified lexical areas it is better to multiply the two values. The advantage of this approach is that it is not possible to assign high importance values to lexical variants which score high on a single measure only. For example, lexical variants occurring in all locations are highly representative, but not distinctive. Similarly, a lexical variant only occurring in a single location is highly distinctive, but not representative (unless the cluster contains a single location). Note that constraints on isogloss construction were also foreseen by Labov, Ash & Boberg (2006) by enforcing frequency thresholds. However, the advantage of the approach proposed by Wieling & Nerbonne (2011) and its evolution presented

here consists in the fact that no *a priori* constraints on the values of individual measures are defined.

### **4 Results**

In this section, we report the results of applying the HBSGP method to the selected ALT dataset. The results obtained are based on 5,174 CL pairs and 213 locations, which correspond to all lexical data gathered through fieldwork (as opposed to a dataset in which infrequent lexical variants are filtered out) for the 170 selected concepts. See Wieling & Montemagni (2015) for a discussion of the advantages connected with this dataset.

The map in Figure 1 shows the geographic visualization of the clustering of Tuscan varieties into seven groups designated as follows: the Florence area (A), the western Tuscan area (C) and the dialects from Arezzo, Siena, Grosseto and

Figure 1: Geographic visualization of the clustering of Tuscan varieties into seven groups.

### 7 Tracking linguistic features underlying lexical variation patterns

Mount Amiata (E) which represent the three main groupings, together with the dialects from Elba island (D), Chiana Valley (F), Capraia Island (G) and Apuan Alps (B) which are minor but clearly distinct dialectal areas.

It is interesting to note that this result is in line with the classifications of Tuscan dialects proposed by Giacomelli (1975) for what concerns the lexicon, and by Giannelli (1976 [2010]) which is based instead on phonetic, phonemic, morphosyntactic and lexical features. It is also in line with the subdivision of Tuscan dialects by Pellegrini (1977), in spite of it being mainly based on the distribution of phonetic phenomena.

### **4.1 Linguistic features underlying identified lexical areas**

For what concerns the underlying lexical features, we first focus on the three main dialectal clusters (A, C and E). Table 2 reports for each cluster the five most important CL pairs with associated values of representativeness, distinctiveness and importance.

The relevance of the lexical features with respect to the dialectal subdivision emerges clearly from the value maps in Figure 2, which show the geographic distribution of the first and second topmost lexical features of each of the three

Table 2: The five topmost lexical variants for the three main clusters of Tuscan dialects.

### Simonetta Montemagni & Martijn Wieling

Figure 2: Value maps of the first (row 1) and second (row 2) topmost CL pairs for the A, C and E dialectal clusters. Areas with darker (blue) color denote a greater frequency of occurrence of the selected lexical variant; lighter colors denote a lower frequency, while no coloring (white) denotes the absence of the variant.

main identified clusters (A, C and E). The topmost lexical features associated with each identified cluster can be assimilated with the traditional notion of bundle of isoglosses, which have long been considered a major criterion for the definition of dialect areas: as Chambers & Trudgill (1998) put it, "the significance of a dialect area increases as more and more isoglosses are found which separate it from adjoining areas".

By comparing the maps of Figure 2, we can observe that the geographic distribution of the topmost CL pairs of the E, A and C clusters does not cover all and

### 7 Tracking linguistic features underlying lexical variation patterns

only the locations in the cluster. Each of them can be seen as a quantitative visualization of individual isoglosses, where darkness of color denotes the frequency of occurrence of the represented lexical variant (dark colors denote a greater frequency, lighter colors lower frequency, and no coloring indicates the absence of the variant). As can be observed, lexical variants shown in Table 2 may occur beyond the border of the cluster area, thus lowering the distinctiveness value of the CL pair, or they may not occur in the whole cluster area resulting in a lower representativeness. For instance, in cluster A comparable representativeness values are observed for the two topmost CL pairs (0.77-0.78), whereas the CL ranked in second place, i.e. ladle-*romaiòlo*, has a lower distinctiveness value (0.53) than the topmost CL (i.e. whose distinctiveness value is 0.67). Different patterns can be observed in clusters E and C, with decreasing representativeness and increasing distinctiveness in the former case, and with both of them decreasing in the latter case. Despite these slight differences, in all cases representativeness and distinctiveness show relatively high values which never reach the value of 1 (with the only exception of the CL pair turkey-*tàcco* in cluster A whose distinctiveness is equal to 1). The average values of the five topmost lexical features for representativeness and distinctiveness range between 0.61 and 0.74, and 0.69 and 0.77 respectively, demonstrating that the corresponding dialect areas are not marked by very clear and strong dialect borders.

Different distinctiveness-representativeness patterns are observed in the case of the smaller peripheral areas B, D, F and G (see Table 3). Here, the most salient CL pairs are highly distinctive (their average values ranges from 0.84 to 1), with the average representativeness ranging from 0.49 to 1. Thus smaller dialect areas are characterized by much more distinctive features than the larger areas.

Besides the strength of dialectal borders, granularity of the identified dialectal areas is another open issue in the study of dialectal variation. Consider, for instance, the traditional dialectal subdivision of Tuscan dialects by Pellegrini (1977) and Giannelli (1976 [2010]). In his *Carta dei Dialetti d'Italia,* Pellegrini (1977) identifies a western variety of Tuscan which is further subdivided into Pisano-Livornese-Elbano, and Pistoiese and Lucchese. On the other hand, Giannelli (1976 [2010]) identifies Pisano-Livornese, Lucchese, Elbano and Pistoiese as independent dialectal varieties in his seminal work *Toscana*. The two subdivisions are compatible with each other but adopt different levels of granularity, i.e. they are seen through lenses differing in their magnifying power. Depending on the specific goals of a study, different levels of granularity of the dialectal landscape may be appropriate. By exploiting the hierarchical clustering results, the HBSGP method can also be used to identify increasingly smaller dialectal areas associ-

Table 3: The five topmost lexical variants for the smaller peripheral areas F, B, D and G.

Figure 3: Geographic visualization of the clustering of Tuscan varieties into two, four and seven groups.

### 7 Tracking linguistic features underlying lexical variation patterns

ated with progressively more specific lexical features. These nested dialect areas are characterized by nested isoglosses (i.e. the spatial distribution of one feature is entirely contained within that of another). To assess these nested isoglosses, we compare the geographical and linguistic results obtained by clustering the selected dataset into two, four and seven groups (with the latter representing the clustering discussed so far).

Figure 3 reports the geographic visualization of clustering the Tuscan varieties into two, four and seven groups. In the map with two clusters (Figure 3, left), the large red cluster corresponds to the composite set of Tuscan dialects, excluding only the Chiana Valley dialects (cyan cluster). The map with four clusters (Figure 3, middle) shows the main subdivision of Tuscan dialects between Northern dialects (cyan and green clusters), covering (from east to west) Fiorentino, Pistoiese, Lucchese and Pisano-Livornese, and Southern dialects (violet and red clusters), i.e. (from east to west) the dialect from Arezzo, Siena and Grosseto (violet cluster) and from the Chiana valley (red cluster). The map containing seven clusters (Figure 3, right) has already been discussed above.

Table 4 shows the lexical features characterizing the red, cyan and pink clusters in the first, second and third map, respectively. These clusters cover a progres-

### Simonetta Montemagni & Martijn Wieling

sively restricted area. Table 4 reports, for each of these clusters, the five topmost lexical variants with their associated scores. The most salient CL pairs characterizing the red cluster of the two-clusters map coincide with pan-Tuscan words well known from the literature (Giacomelli & Poggi Salani 1984): they show a distinctiveness value equal to 1 and very high representativeness values (*≥* 0.79). Similar observations hold for the cluster corresponding to the set of Northern Tuscan dialects (the cyan cluster in Figure 3, middle) with one main difference: all values are considerably lower, with a general reduction observed at the level of representativeness. This illustrates that the cyan cluster is a heterogeneous area. However, by comparing the CL pairs underlying the cyan cluster in the second map and the pink cluster in the third map, we can also see there are two shared lexical variants, namely oil jar-*cóppo* and eye gum-*cìspia*, which appear among the topmost features whose importance values in the smaller pink cluster are higher (determining a higher ranking), despite their unavoidably lower distinctiveness. In this case, these CL pairs are more characteristic of the smaller cluster, whereas a word such as thimble**-***anèllo* is more characteristic of the larger cluster (in the pink cluster it appears in a lower position with much lower values). This suggests that whenever the same features appear to qualify nested clusters, they should be taken as relevant features for the cluster in which they play a more prominent role (i.e. having a higher importance value). Consequently, oil jar*cóppo* and eye gum-*cìspia* should be removed from the most salient features of the cyan cluster due to the lower importance (0.424 against 0.522 for the former, and 0.397 against 0.474 for the latter) with respect to the nested pink cluster.

In sum, these results show that hierarchical spectral partitioning can be usefully exploited to identify dialectal areas at different levels of granularity with their associated lexical features. In particular, the method may help in the selection of the most appropriate isoglosses for each dialectal area and in the reconstruction of nested isoglosses.

### **4.2 Reconstructing the dynamics of lexical change**

The hierarchical spectral partitioning method can also be used for studying the dynamics of lexical change across generations. For this purpose, ALT speakers were grouped in an old age group (born in 1930 or earlier – 1930 was the median year of birth) and a young age group (born after 1930). To guarantee comparability of results, we focused on two maps each having four clusters. As Figure 4 shows, the analysis of the two datasets results in slightly different, partially overlapping lexical areas, with the area corresponding to the southeastern (cyan) cluster being more restricted for the older speakers. Major differences, however, are

### 7 Tracking linguistic features underlying lexical variation patterns

Figure 4: Geographic visualization of a four-way clustering of Tuscan varieties on the basis of data from young vs. old speakers.

explicitly clear at the level of the underlying lexical features. In particular, the central blue area is more restricted (and also linked with fewer CL pairs: 881 vs. 1193) in the map built on the basis of the answers by the young speakers.

Besides the different size of the set of associated linguistic features (i.e. more reduced in the case of young speakers), it is interesting to note that 424 salient lexical features underlying the old speakers map do not appear among the features underlying the young speakers map. These CL pairs emerging from old speakers correspond typically to old-fashioned and traditional notions as well as less common plants and animals. Examples include structure for bed warmer*prète*, poppy-*ròsolo*, mutton-*bìrro*,set of poplars-*alborellàia*. These CL pairs can be seen as lexical variants which are no longer being used by younger speakers, and these are likely to disappear altogether.

The number of CL pairs restricted to young speakers is much lower (112) than the number of CL pairs restricted to the old speakers. In this case, the CL pairs correspond to standard Italian words (e.g., closet-*ripostìglio*, weeping willow*sàlice piangènte*, harvest-*mietitùra*), generic terms (e.g., afternoon-*dópo mangiàto*, slug-*lumàca ignùda*) or "distorted" (i.e. deviant with respect to traditional pronunciation) variants of dialectal terms (e.g., Tuscan cold cut from pork shoulder-*capricòllo*). The typology of these lexical variants shows the dynamics of lexical change ongoing in younger Tuscan generations, characterized by the loss of local features in favor of generic or standard terms, and by the creative distortion of dialectal words.

In both cases, however, these CL pairs are not highly ranked (i.e. not the most

### Simonetta Montemagni & Martijn Wieling

important) for the associated old and young clusters. Instead, the CL pairs underlying both maps (a total of 769) show clear differences with respect to their ranking. For example, the 1st, 10th, 20th and 50th lexical variants in the ranked list of CL pairs underlying the old speakers map correspond to the 60th, 809th, 59th and 818th position in the young CL pairs list, respectively. Similarly, the 1st, 10th, 20th and 50th ranked lexical variants of the young speakers are ranked (respectively) in the 100th, 13th, 17th and 69th position in the old speakers list. The asymmetry between the old-young vs. young-old correspondences can be seen as the result of a dialect leveling process, causing the lower importance of old-fashioned lexical variants for the young speakers (which are top-ranked for the old speaker). Seen from the perspective of young speakers, the disalignment of the ranking is more reduced, reflecting an additional shared set of dialectal lexical items.

Table 5 reports the five topmost CL pairs underlying the blue cluster in the two maps. Clearly, the importance values associated with the blue cluster of the old speakers are higher than those associated with the blue cluster of the young speakers. This pattern is confirmed by comparing the average importance scores of the top-10 and top-100 CL pairs in the two lists, which are much higher for the old speakers (0.42 vs. 0.34 for the top-10 and 0.26 vs. 0.17 for the top-100). This may also be seen as evidence in support of dialect leveling: lexical areas inferred from young speakers data are characterized by less distinctive and/or representative features.

Table 5: The five topmost lexical variants of the blue cluster in the young vs. old speakers maps of Tuscan dialects.

### **5 Conclusion**

In this paper, we illustrated the application of hierarchical spectral partitioning of bipartite graphs in the study of lexical variation in Tuscany based on the dialectal corpus of the *Atlante Lessicale Toscano*. Our results demonstrate the potential of the method in bridging the gap between models of linguistic variation based on aggregate analyses and more traditional analyses based on individual linguistic features.

By using the HBSGP method, we not only identified existing patterns of lexical variation in Tuscany on the basis of the whole dialectal corpus, but also uncovered the underlying lexical features in terms of the characterizing conceptlexicalization pairs. The most relevant CL pairs represent the features used to classify and define each identified lexical area. To put it in more traditional terms, they can be seen as a proxy of lexical isoglosses marking both the qualitative and quantitative distribution of the lexical variants identified as discriminating features of a given lexical dialect area. This entails that the set of the topmost CL pairs associated with each identified lexical dialect area acts as a proxy of bundles of isoglosses, where the grading of individual isoglosses within the bundle is determined on the basis of the combination of representativeness and distinctiveness. If the representativeness score associated with identified isoglosses (CL pairs) can help to shed light on how much variation exists within the area defined by a given isogloss, the distinctiveness score reflects how strongly the lexical variant is concentrated within that area. By comparing the results obtained for different dialect areas, we have seen that different stages of the process of dialect differentiation can be inferred from the different values of these two measures: dialectal subdivisions range from clearly defined areas to areas characterized by fuzzy borders.

We also investigated whether and to what extent patterns of lexical variation and their associated features varied with respect to the granularity of the identified dialectal areas and with the age of informants, revealing interesting results. The possibility of exploring linguistic variation at different levels of granularity makes it possible to customize the analysis with respect to the user's needs. The linguistic features associated with increasingly smaller areas can be seen as nested isoglosses, occurring when the spatial distribution of one feature is contained entirely within that of another and establishing an implicational relationship between the two.

The analysis and comparison of lexical variation patterns and associated features across generations showed that the method can also be usefully exploited

Simonetta Montemagni & Martijn Wieling

to track the change in the typology of features in young vs. old informants and to monitor the vitality of a dialect in a given area. In particular, the HBSGP method turned out to effectively capture the dynamics of lexical change in Tuscany, by highlighting the emergence of lexical innovations and the obsolescence of old-fashioned traditional dialectal words.

Current directions of research include testing the robustness of these results by noisy clustering and the analysis of lexical variation patterns across semantic domains.

### **Acknowledgements**

The research reported in this article was carried out in the framework of a Short Term Mobility program of international exchanges funded by the National Council of Research (CNR, Italy). The authors thank the anonymous reviewers for their comments, which have helped to improve this article.

### **References**

Giacomelli, Gabriella. 1975. Aree lessicali toscane. *La ricerca dialettale* 1. 115–152.

Giannelli, Luciano. 1976 [2010]. *Toscana*. Pisa: Pacini Editore.

Grieve, Jack, Dirk Speelman & Dirk Geeraerts. 2011. A statistical method for the identification and aggregation of regional linguistic variation. *Language Variation and Change* 23(2). 193–221.

### 7 Tracking linguistic features underlying lexical variation patterns

### **Chapter 8**

## **A new dialectometric approach applied to the Breton language**

Guylaine Brun-Trigaud CNRS & Université de Nice-Sophia-Antipolis, France

Tanguy Solliec Université de Paris Descartes, France

Jean Le Dû Université de Bretagne Occidentale, France

> This paper presents a new dialectometric approach applied to the Breton language in the heart of Breton-speaking Brittany. It is based on data from the Nouvel Atlas Linguistique de la Basse-Bretagne Le Dû (2001). We process qualitative data using the Levenshtein algorithm which allows us to accurately measure and take into account the discrepancies or similarities between different pronunciations of a given word. This contribution aims to determine whether linguistic distance is caused by a frequent repetition of the same phenomenon or whether it is the outcome of multiple changes. Our first results suggest new ways of analysing Breton data.

### **1 Introduction**

At a time when the number of languages in use is bound to reduce drastically in the next decades, documenting endangered languages is a major challenge for linguists, as is the urgency to study and analyse their internal variation. Nowadays, Breton is considered to be an endangered language, according to the definition proposed by UNESCO (Moseley 2010). The intergenerational transmission of the language ceased during the 60's. Most of its speakers are now over 70 years of age and, at present, the language is disappearing quickly. We propose to present a dialectometric approach applied to the Breton language based on Le Dû's (2001)

Guylaine Brun-Trigaud, Tanguy Solliec & Jean Le Dû. 2016. A new dialectometric approach applied to the Breton language. In Marie-Hélène Côté, Remco Knooihuizen & John Nerbonne (eds.), *The future of dialects*, 135–154. Berlin: Language Science Press. DOI:10.17169/langsci.b81.147

### Guylaine Brun-Trigaud, Tanguy Solliec & Jean Le Dû

*Nouvel Atlas Linguistique de la Basse-Bretagne* (henceforth NALBB) (Le Dû 2001) so as to re-examine the geolinguistic landscape of Lower Brittany.

Little dialectometric work has been done on the Breton language (German 1984, German 1991, Costaouec 2012). Our approach is based on a new methodology: we have applied the principles of edit distance (i.e., the Levenshtein Distance, henceforth LD), which allows us to measure the linguistic distance between two strings of characters (phonetic transcriptions). This distance is defined as the minimal number of characters that need to be deleted, inserted or replaced, in order to transform one string of characters to another. We have introduced modifications in processing the data.

Our work aims to test this new approach and to observe its advantages and disadvantages when applied to Breton. It was tested for the first time on dialects in the Occitan area. This research constitutes the very first step of a PhD thesis by Solliec (2014), whose goal is to process the data available in the NALBB in order to sketch the geolinguistic configuration of the area based on a dialectometric approach. This first study focuses on the phonetics of Breton because the data contained in the NALBB are richer in phonetics than in the lexicon.

### **2 Preliminary research**

### **2.1 A first test on Occitan dialects**

Dialectometry was initiated by Séguy (1971; 1973), and Guiter (1973). Its aim is to quantify linguistic variation with the help of mathematical tools. This approach allows us not only to account for the differences or similarities between the dialects studied, but also to display them with the help of maps and tables. Nowadays, many research groups work with various methods. One of them, the Groningen School, led by Nerbonne and Heeringa (Nerbonne & Heeringa 2001; 2010, Heeringa 2004), uses the Levenshtein algorithm. This methodology allows for the accurate measurements of the number of operations (replacement, deletion or insertion) necessary to transform one string of characters to another and displays the results as a number or a percentage of similarity.

As this method is intended to provide an overall characterization, it accounts most of the time for dialectal distance from a quantitative point of view. However, it does not describe the results qualitatively in a direct way. In others words, it does not indicate the exact proportion of each difference involved in dissimilarity. Does the dissimilarity result from one sole phoneme correspondence (replacement) which occurs frequently while other kinds of variations remain very

### 8 A new dialectometric approach applied to the Breton language

minor or, on the contrary, does the dissimilarity result from an important array of different correspondences which are more or less of equal importance? In the first case, mutual comprehension between the speakers is not really affected, while in the second case, it is likely to be jeopardized. In order to contrast the two kinds of variation, Brun-Trigaud decided to modify the algorithm. To accomplish this, she implemented it as a function in Visual Basic for Applications (VBA) in an Excel spreadsheet so as to obtain, not a percentage or a number of characters, but rather a record of the phonemes undergoing the operations and their frequency. She then treated them statistically. She carried out a first experiment (Brun-Trigaud 2014) using data taken from the *Atlas Linguistique et Ethnographique du Languedoc Occidental*, gathered in a region located in the Occitan area in southern France. This language area is distinguished by both a large and relatively homogeneous central zone around the city of Toulouse and by two mixed zones, one to the North in contact with Limousin and the other to the South, bordering the Catalan-speaking area.

Table 2: An example of a qualitative measurement between 2 locations of enquiry for Occitan

When all the data taken from two different locations were compared, as in Table 2, she obtained: replacing r by ʀ (= 36%) and replacing ɔ by œ (= 22%). Brun-Trigaud demonstrated that, for the area studied, from East to West, divergences were caused by the preponderance of a single phonetic correspondence, appearing very frequently, whereas in the North of the area differences in nuGuylaine Brun-Trigaud, Tanguy Solliec & Jean Le Dû

merous phonetic variables appear, which affects mutual understanding between speakers of different areas.

In order to validate these initial results, she invited her fellow Breton dialectologists, Solliec and Le Dû, the author of the NALBB, to test the new version of the algorithm on Breton data.

### **3 Earlier dialectometric works on Breton**

Studies by Falc'hun (1981) based on the *Atlas Linguistique de la Basse-Bretagne* (Le Roux 1924–1963, henceforth ALBB) have deepened the geolinguistic understanding of the Breton language. They constitute an important reference on the issue.

Figure 1: Situation of the Breton language.

Until now very little quantitative work has been done on the Breton language. German (1984; 1991) is the very first to have applied a dialectometric approach to Breton data. A cluster analysis using the Lerman algorithm allowed him to classify the dialect areas according to their linguistic similarities and differences with the Breton of Saint-Yvi he described in his PhD thesis (1984). More recently

### 8 A new dialectometric approach applied to the Breton language

Costaouec (2012) correlated the internal dialect borders inside a small area of the Breton speaking region with other aspects of social, cultural or economical factors around the village of La Forêt-Fouesnant, whose speech he described in his thesis (Costaouec 1998). These two studies are based on Le Roux's ALBB, where the data were collected between 1913 and 1920 for a range of 87 sites of enquiry published in 600 maps. Le Dû began working on his NALBB (2001) in 1968 (187 sites and 601 maps). He himself transcribed all the data in order to avoid transcription biases. Since no recent study has tackled the Breton language as a whole, as we mentioned before, further works by Solliec will aim to fill this gap. In order to accomplish this, he will take into account the NALBB data. This article constitutes the first step in this process.

### **4 The area investigated**

We have deliberately chosen to investigate a restricted and quite homogeneous area for the following reasons: on the one hand, we do not want to confront this modified version of the LD algorithm, from a technical point of view, with an area where linguistic variation is very intense, as in the case of the *Vannetais* dialect (South-East of Lower-Brittany). Moreover, as was shown by Falc'hun (1981), innovations have been spreading across this region for centuries. It would then be interesting to compare them with sites from more conservative peripheral areas. Over the past few years, Solliec has been carrying out a project to describe and document local varieties in this area and is therefore aware of the local variation phenomena.

On the methodological side, we have decided to use an inter-site approach: to do so, each location of the area is linked to its closest geographical neighbours in order to establish a comparison we called the "segment". Doing so allows the researcher to detect spatial continuities and discontinuities (breaches) on the phonetic level (Goebl 2012: 137). In this view we selected 23 investigation sites for 165 phonetic maps (i.e. using only one lexeme and its phonetic variations for each concept). We had not beforehand determined which variables to observe in our corpus: our objective was to consider the data as a whole and to observe which phenomena produce linguistic distance and at which frequencies.

It led us to divide the area into a net of 53 "segments", each of which connect a site in the atlas with its neighbours on all sides. This brings us to a total of 8745 comparisons between strings of characters.

Our aim has been on the one hand to gather quantitative indicators and, on the other hand, to identify qualitatively the most common correspondences involved

Guylaine Brun-Trigaud, Tanguy Solliec & Jean Le Dû

Figure 2: NALBB Map 411 'Fingernails'. The area investigated is delimited in black.

in phonetic variation. Even though the latter are well known (Falc'hun 1981; Jackson 1967), we do not know how frequently they occur.

### **5 The problems LD meets with Breton**

In confronting LD with Breton language data, we noticed difficulties: Table 3 shows an excerpt of the data where the algorithm repeatedly failed to analyze the data, while at the same time the FuzzyMatch function did not work as we can see in Table 3. This last function returns a percentage reflecting the probability of correspondences (and differences) between two words.1

1 See https://x443.wordpress.com/2012/06/25/fuzzymatch-in-vbavbscript/

### 8 A new dialectometric approach applied to the Breton language

Table 3: Examples of the difficulties encountered by LD for the concept 'that one (masc.)'

Although we were careful to use a unique character for each phonetic segment and its diacritics (including diphthongs and geminates), it appears that our implementation of the Levenshtein algorithm experiences difficulties when treating languages like Breton for which variation in the number of syllables is frequent (e.g., site 80 Berrien [ˈparo̝s] vs. site 81 Poullaouen [pa:ʀs] 'parish' (NALBB map 7)).

Nevertheless, after checking and adjusting the function, we ended by incorporating most observations. On the one hand, the raw results obtained from the FuzzyMatch function produces the following distribution (see map in Figure 3):

The map shows several areas involving significant degrees of similarity, particularly in the North around sites 24 on the one hand and 39 on the other, and in the South around site 88. It is clear that the geographic distance between the surveyed localities often correlates with phonetic similarity; for example, there is high phonetic similarity between nearby sites 87 and 88 and low similarity between distant sites 83 and 93. However, there are some exceptions, for instance, between sites 22 and 24 or, vice versa, between 91 and 89. We will later see why they occurred.

On the other hand, the results returned by the new function of the algorithm bring us to the following conclusions: first of all, amongst the 11,949 non-identical phonetic correspondences, "alternations" or "differences" according to our terminology, we had to manage an important dispersion of the distinctive correspondences (nearly 450). They were generated by the narrow phonetic transcription. With the consent of Le Dû, the author of the atlas, we grouped some alternations in order to have to deal with fewer details. This limited the number of the different kinds of alternations to 200. The most frequent ones are listed in Table 4.

These alternations are distributed as follows on the map (Figure 4): for each segment (i.e. a pair of enquiry sites) we calculated the most frequent alternation proportional to its frequency (identitical correspondences are excluded).

Figure 3: FuzzyMatch results for the area on a schematic map

Table 4: List of the most frequent alternations

8 A new dialectometric approach applied to the Breton language

Figure 4: Distribution of the most frequent alternations across the area

Apart from the [r] variations, the proportions of changes are relatively small, but they are quite similar to the results found in the Occitan region, with one notable difference: there were far more changes in the consonants Brun-Trigaud (2014: 135). In addition, as noted by Le Dû, [r] variations are probably idiolectal, so that we consequently decided to concentrate on other more relevant changes with his agreement.

The nature of the differences is more varied in the second most frequent alternation (map in Figure 5).

Taking into account the fact that the proportions of changes are relatively low – 3 to 13% at most — the map in Figure 5 shows that the south-eastern area is marked by the presence or absence of schwa (dark colour), the central area by alternations between [a] and [ə] (white colour), and, finally, that the north-west is characterized by an alternation between [e] and [ə] (dark grey).

### **6 Analysing the first results**

The values the algorithm returned provided a considerable amount of data. They had to be explored carefully. The following analyses are instances of what can be done when scrutinizing geolinguistic data from a statistical perspective when

Guylaine Brun-Trigaud, Tanguy Solliec & Jean Le Dû

Figure 5: Distribution of the second most frequent alternation across the area

associated with a qualitative approach. First, we will observe how one specific alternation occurs across the area, involving [a] and [ə]. Secondly, we will study the results for one locality, Collorec, site 88 of the NALBB.

### **6.1 Examining only one kind of difference across the area**

The most important kind of change from a numerical point of view, apart from the variation of the rhotics, is the one which affects the vowels [a] and [ə]. In our corpus, this alternation ([a]/[ə])2 constitutes 9% of the non-identical phonetic correspondences we have gathered and appears in 54 of the 165 maps we selected.

The realizations [a] or [ə] occur in the following phonetic contexts. Each one can interplay with another:

Eighteen of the 165 maps we used included a definite article and 11 had infinitive word forms. These are instances of the alternations under study (see examples a. and e. in Table 5).

2 The alternation is indicated by the brackets ( ) in order to distinguish it from the specific sounds [a] and [ə]

### 8 A new dialectometric approach applied to the Breton language

Table 5: Phonetic contexts for the alternations ([a]/[ə])

This strong presence may result from our selection of maps but, on the other hand, these alternations are very frequent in this language. On a more general level, ([a]/[ə]) alternation is interesting because it offers a perspective on the degree of centralization of vowels by Breton speakers. Breton final-syllable vowels tend to be centralized when unstressed, especially in the central dialect area (Wmffre 1998: 8–10). The occasional appearance of a clear [a] in post-stress context goes against the general economy of the language (Martinet 1955).

The ([a]/[ə]) alternation occurs regularly but with only a slight number of occurrences across the area. The data for this alternation can be classified according to their frequency of appearance in each location under enquiry. Three zones can be spotted according to the spatial distribution of the difference under examination (see Table 6 and Figure 6).

This kind of difference occurs the most intensely in the central area in dark grey. In the two other areas located on the fringes, the frequency is less important. At present, it is still difficult to draw a conclusion because we do not understand how this small area is connected to the rest of the Breton-speaking region, continuously or discontinuously.

Finally, a closer look at these results led us to identify a particular realization of the kind that occurs recurrently. Some words may end with an [a] and a final consonant (a+Cons.) in post-stressed contexts when a more common realization with a schwa and a final consonant (ə+Cons.) would have been expected as in the

Table 6: Values for the alternation ([a]/[ə])

8 A new dialectometric approach applied to the Breton language

Figure 6: Intensity of the alternation ([a]/[ə]) across the area & locations with a high number of specific realizations

following example: NALBB map 109 'a day (long)' site (33) Plougonven [ˈde:vas] ̝ vs. site. (80) Berrien [ˈde:vəs]. ̝

This specific realization operates in a quite clear context especially in nouns and in the past participle forms of the verbs. This alternation cannot be explained by etymology.

The realization of [a] for [ə] in final unstressed syllables occurs only in 45 cases in this category that is, 4.26% of the total of the alternations ([a]/[ə]) and only 15 different lexemes in our corpus were affected by this specific realization. But it occurs in one little area and this specific realization is distributed as Table 7 shows.

This appears to be simply anecdotal on the surface but this specific realization regularly occurs in our corpus. Interestingly, the phenomenon is concentrated in the same micro-area around Plougonven (site 33). This means that this specific and not so common realization of a final unstressed (a+Cons.) could be salient from a sociolinguistic perspective as a sign of local identity and explain why an additional unneeded articulatory effort after the stressed syllable is produced for some words. Nevertheless, only a deeper scale study on these issues could help us to better understand the centralization of vowels in Breton.

### Guylaine Brun-Trigaud, Tanguy Solliec & Jean Le Dû

Table 7: Distribution of the different occurrences of (a+Cons.) according to the location investigated

### **6.2 The differences around one locality**

Another approach is a close examination of the data for one location in order to determine the phonetic similarity between neighbouring sites. We decided to do this for Collorec (site 88), which is worth analysing because of its centrality and on account of the large number of results gathered for this place: 1354 alternations. They account for nearly 11% of all the non-identical correspondences we observed in our corpus. This number is far higher than the average of changes for each locality (519) of the area. The following figures display the linguistic distance between Collorec and its closest neighbouring locations from a phonetic point of view.

Table 8 shows how the differences observed between Collorec and its neighbouring sites are represented spatially. For each segment the number of alternations we noticed corresponds to the average observed for all the other segments across the area (225 on average).

Figure 7 shows that the level of intensity with respect to the differences varies according to the site-pair involved: three locations (80, 82, 90) are phonetically more distant from Collorec than the others. The distance we have noticed for sites 80 and 90 could be explained by a geographic factor: a bog named *Yeun Ellez* spreads out between these sites and Collorec. These findings about phonetic traits match Solliec's own research in the area. This marshy bog, which is an obstacle to travel, seems to promote linguistic differences more than the hills

Table 8: Number of differences to neighbouring sites around Collorec (site 88)

Figure 7: Number of differences to neighbouring sites around Collorec (point 88)

Guylaine Brun-Trigaud, Tanguy Solliec & Jean Le Dû

(named *Monts d'Arrée*), on the North-Western edge of the area, which are easy to get over (see Figure 3: FuzzyMatch map).

Moreover, through the statistical use of the data returned by the LD algorithm, the features of the linguistic variation in Collorec can be examined. The alternations observed for Collorec are detailed in the following figures.

Figure 8: Nature of the alternations between Collorec and its surrounding neighbours

The diagrams 8 and 9 show how the non-identical phonetic correspondences we found between Collorec and its surrounding locations are distributed. As these figures show, two types of alternations can be distinguished. The first one groups together the alternations that only show up once or twice in the results. In the second category are gathered the alternations, which can be clearly individualized (Figure 9) and which happen on a quite frequent basis.

These alternations are the same as those occurring across the whole area and with the same frequency, such as the different kinds of rhotics or the ([a]/[ə]) correspondence. Nevertheless, one must keep in mind that the number of occurrences for each alternation is low. For instance, 20 occurrences out of 1354 only account for 1.5% of them. We did not find any alternation unique to Collorec in spite of the large amount of data we have at our disposal for this specific location,

Figure 9: Nature of the alternations between Collorec and its surrounding neighbours

whose features thus share in the general tendencies of the area, which is quite uniform in phonetic terms.

These two different approaches, the first about a specific alternation and the second dealing with the phonetic features of one location are the type of investigation opportunities this new usage of the LD algorithm offers for geolinguistic research.

### **7 Conclusion**

To conclude, we want to stress that the sample of data used for this investigation was small because we deliberately restricted the size of the area under investigation as well as the amount of data. This paper reflects the first step of a more ambitious research to present a dialectometric analysis covering the whole Breton-speaking area. Our specific use of the LD algorithm allows us to treat qualitative data statistically. Linguistic variation can be described from a quantitative viewpoint while taking into account the specific features at stake so as to present a more comprehensive view of the data under analysis.

Furthermore, this new method allows us to divide the results obtained into 3 main categories: vowels, consonants and rhotics. These categories will have to be analysed individually while keeping in mind they can interact. A further step to this research will be to elaborate a dialectometric analysis on a larger scale, based on the data of the NALBB, being conducted by Solliec as part of his PhD at the University of Brest in France. For this purpose, he will compare different dialectrometric techniques. The Breton-speaking area is interesting as it constitutes a real linguistic laboratory thanks to its important inner variations and to its distinctiveness from the neighbouring Romance varieties.

### **Acknowledgement**

We would like to thank Professor Gary German (UBO Brest) for his precious help in rereading and revising this paper.

### **References**

### **Chapter 9**

## **Automatically identifying characteristic features of non-native English accents**

### Jelke Bloem

Amsterdam Center for Language and Communication, University of Amsterdam

### Martijn Wieling

Center for Language and Cognition, University of Groningen

### John Nerbonne

Center for Language and Cognition, University of Groningen, Freiburg Institute for Advanced Studies, University of Freiburg

We demonstrate the application of statistical measures from dialectometry to the study of accented English speech. This new methodology enables a more quantitative approach to the study of accents. Studies on spoken dialect data have shown that a combination of representativeness (the difference between pronunciations within the language variety is small) and distinctiveness (the difference between pronunciations inside and outside the variety is large) is a good way to identify characteristic features of a language variety. We applied this method from dialectology to transcriptions of the words from the Speech Accent Archive, while treating L2 English speakers with different L1s as 'varieties'. This yields lists of words that are pronounced characteristically differently in comparison to native accents of English. We discuss English accent characteristics for French, Hungarian and Dutch, and compare the results to other sources of accent information. Knowing about these characteristic features of accents has useful applications in teaching L2 learners of English, since potentially difficult sounds or sound combinations can be identified and addressed based on the learner's native language.

### **1 Introduction**

Dialectologists have taken advantage of computational techniques to study regional language variation, and developed specific measures for quantifying this

Jelke Bloem, Martijn Wieling & John Nerbonne. 2016. Automatically identifying characteristic features of non-native English accents. In Marie-Hélène Côté, Remco Knooihuizen & John Nerbonne (eds.), *The future of dialects*, 155–173. Berlin: Language Science Press. DOI:10.17169/langsci.b81.148

### Jelke Bloem, Martijn Wieling & John Nerbonne

variation. This field of quantitative dialectology is known as dialectometry. Dialectometry research has led to a variety of methods for analyzing large numbers of dialectal features in systematic ways. In particular, aggregation of features made available new methods such as quantification of distances between dialects, and statistical analysis of differences that allowed generalization over the noise inherent in examining any single linguistic feature (Nerbonne 2009). However, dialectologists are still interested in examining single features as well. Typical characteristics of dialects, known as 'shibboleths', are quite salient and frequently discussed among both dialectologists and laymen. Prokić, Çöltekin & Nerbonne (2012) show that quantitative methods can provide insight into this phenomenon as well. They identify the most characteristic words for various Dutch dialects, providing statistical evidence due to the aggregation of data.

The methods that have been developed in dialectometry have not been widely applied to other domains of linguistics, but there are clear generalizations that can be made. Any time a set of language variants is studied, where the languages differ in a quantifiable way, dialectometry methods can potentially be applied. This is certainly the case in second language acquisition, where different language backgrounds lead to a lot of variety among learners. In the acquisition of a particular second language such as English, native Mandarin speakers will produce a different English than native German speakers. These kinds of differences can be studied with dialectometric methods.

In particular, researchers working on accent studies (i.e. Wells 1982; Waniek-Klimczak 2008) could benefit from the use of these methods. It has long been noted that foreign accents can be perceived negatively (Ryan 1983). As a consequence, pronunciation training is a part of second language teaching, in which the goal is to make the students' accents more native-like. Since it is quite difficult to achieve native-like proficiency in second language learning, it has long been acknowledged that learners do not need to learn how to speak perfectly, but that intelligibility is sufficient:

The learner (…) would have presented to him certain carefully chosen features on which to concentrate, the rest of his pronunciation being left to no more than a general supervision (Abercrombie 1956: 93).

This suggestion has later been developed into the idea of a hierarchy of errors, i.e. pronunciation problems that require the most attention in pronunciation training. A summary of research in this direction is provided by van den Doel (2006: 7–15). He notes that such hierarchies "have been formulated partly on the basis of experimental research, but mainly as a result of impressionistic observational procedures". Obviously, they are also language-specific.

### 9 Automatically identifying features of non-native English accents

We are not aware of many studies that discuss error hierarchies of phonological errors, or characteristic feature rankings. One example of the use of error hierarchies in a more general sense can be found in Rifkin (1995). This analysis does not go to the level of phonological features, as it discusses grammatical errors and intonation errors. Gynan (1985) discusses phonological features and places them in an error hierarchy, but only on a general level. Based on data from Spanish learners of English and U.S. bilingual native speakers of English, he notes that comprehensibility of accents is related more to phonological than to morphosyntactic characteristics, but problems with morphosyntax are more salient to native speakers.

There are also studies that discuss characteristic pronunciation errors in English by speakers of a specific language. Gao (2005) studied a Chinese L2 student of English in a longitudinal study over 12 weeks, analyzing the errors and determining whether they arose from first-language interference or from being in an early stage of language acquisition. Potential errors were identified from earlier work on Chinese accents, a methodology that is strongly biased against the discovery of less stereotypical errors. The study finds that most errors arise from Chinese interference, though this may be partly due to the bias towards typical Chinese errors. The article also notes the need for research that studies a wider range of speakers.

Another line of work that assumes strong interference effects and makes comparisons to native speaker phonology is automatic accent classification. These methods are often also based on the assumption that the non-native speaker replaces unfamiliar sounds in the second language with sounds from their native language, e.g. by Angkititrakul & Hansen (2006).