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#### **References**

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## *Article* **Short-Term Sources of Cross-Linguistic Phonetic Influence: Examining the Role of Linguistic Environment**

#### **Daniel J. Olson**

School of Languages and Cultures, Purdue University, West Lafayette, IN 47907, USA<EMAIL_ADDRESS>Received: 16 August 2020; Accepted: 13 October 2020; Published: 24 October 2020

**Abstract:** While previous research has shown that bilinguals are able to effectively maintain two sets of phonetic norms, these two phonetic systems experience varying degrees of cross-linguistic influence, driven by both long-term (e.g., proficiency, immersion) and short-term (e.g., bilingual language contexts, code-switching, sociolinguistic) factors. This study examines the potential for linguistic environment, or the language norms of the broader community in which an interaction takes place, to serve as a source of short-term cross-linguistic phonetic influence. To investigate the role of linguistic environment, late bilinguals (L1 English—L2 Spanish) produced Spanish utterances in two sessions that differed in their linguistic environments: an English-dominant linguistic environment (Indiana, USA) and a Spanish-dominant linguistic environment (Madrid, Spain). Productions were analyzed at the fine-grained acoustic level, through an acoustic analysis of voice onset time, as well as more holistically through native speaker global accent ratings. Results showed that linguistic environment did not significantly impact either measure of phonetic production, regardless of a speaker's second language proficiency. These results, in conjunction with previous results on long- and short-term sources of phonetic influence, suggest a possible primacy of the immediate context of an interaction, rather than broader community norms, in determining language mode and cross-linguistic influence.

**Keywords:** bilingualism; phonetics; language mode; cross-linguistic influence; transfer; voice onset time; global accent rating

#### **1. Introduction**

Research has shown that bilinguals, including both early bilinguals (e.g., MacLeod and Stoel-Gammon 2005) and late second language learners (e.g., Schmid et al. 2014), can effectively maintain two separate phonetic systems for their two languages. However, these two phonetic systems are not fully independent, and cross-linguistic influence, in which the phonetic system of one language is influenced by the competing language, has been evidenced across a range of bilingual populations and contexts. Importantly, there are a variety of both long-term and short-term sources of cross-linguistic influence (i.e., transfer), impacting both a bilingual's first (L1) and second (L2) languages. Broadly, short-term refers to contexts in which production or perception may be altered for a single speaker in response to immediate or momentary changes in the linguistic situation (e.g., bilingual language mode and code-switching), while long-term refers to sustained influences over longer periods of times (e.g., acquisition and immersion). While some long-term sources of phonetic cross-linguistic influence, such as immersion (e.g., Casillas 2020) and instruction (e.g., Lee et al. 2015), are well-studied, less research has focused on potential short-term (i.e., transient) sources of cross-linguistic influence (Simonet 2014).

Given the previous focus on long-term sources of cross-linguistic phonetic influence, and the emerging research showing the relevance of a number of short-term sources, the current study examines the potential impact of a novel source of such influence: linguistic environment. Linguistic environment is broadly defined as the language norms of the broader community in which an interaction or experimental paradigm is conducted. Bilinguals naturally move from one linguistic environment to another for work, travel, and social interaction, and such shifts in context or environment may serve to foster cross-linguistic influence at the phonetic level. This study adds to our theoretical understanding of the organization of bilingual phonetic systems, and highlights both the sources of and limits on cross-linguistic phonetic influence.

#### **2. Literature Review**

Cross-linguistic influence at the phonetic level can be described as the way in which the phonetic system of one of a bilingual's languages impacts the production and perception of speech sounds in their other language (Jarvis and Pavlenko 2008). Within studies of bilingual phonetics, and as a condition for examining cross-linguistic influence or transfer, early research sought to establish that bilinguals are indeed able to produce and maintain separate phonetic norms in each of their two languages (e.g., Caramazza et al. 1973). While bilinguals produce different phonetic categories for their two languages, the relationship of these categories to the monolingual norms may depend on a variety of factors. For example, some research has shown that bilinguals, particularly early bilinguals, may show little to no deviance from the monolingual targets in each of their languages (e.g., Flege et al. 1999; Guion et al. 2004; Piske et al. 2002), while others have found that late bilinguals (e.g., Flege and Eefting 1987; Flege and Port 1981) and even some early bilinguals (e.g., Flege et al. 1995; Fowler et al. 2008) produce phonetic categories that deviate from those produced by monolingual speakers.

#### *2.1. Long-Term Sources of Cross-Linguistic Phonetic Influence*

In the line of research that has examined cross-linguistic phonetic influence, there has been a significant body of research that has examined the impact of L1 phonetic systems on L2 phonetic categories. Broadly, this line of research has established that the extant L1 system exerts influence over the L2 system, shaping both production and perception of L2 phonetics. Several L2 phonological models provide theoretical accounts for the mechanisms that govern the acquisition of new phonetic categories, and there is broad agreement that the ability to acquire a new L2 category depends on the relationship to existing L1 sounds (e.g., Speech Learning Model (Flege 1987, 1988, 1991, 1995); Native Language Magnet theory (Kuhl 1992, 1993a, 1993b); Perceptual Assimilation Model-L2 (Best and Tyler 2007)). Moreover, as the L2 phonetic system develops as the result of engagement with the L2, either through immersion (for review of study abroad and at-home instruction, see Casillas (2020); for longer-term immigration, see Piske et al. (2001); for study abroad, see Solon and Long (2018))1 or instructed acquisition (for review of L2 phonetic instruction, see Lee et al. (2015)), the cross-linguistic influence of the L1 on the L2 diminishes and the L2 becomes more native-like. As shown in research on study abroad, such effects may be observed following stays of just a few weeks (e.g., Lord 2010), although Solon and Long (2018) note significant individual variation. Following long-term exposure, ultimate phonetic attainment may (e.g., Schmid et al. 2014) or may not (e.g., Flege 1987, 1991) match native speaker norms (for variability in attainment, see Simon (2009)).

Beyond the expected influence of long-term exposure to the L2 on L2 phonetic production, there is also evidence of influence of the L2 on the L1 phonetic system. Several studies have found an inverse relationship between the length of residence in an L2 linguistic environment or L2 proficiency and the degree of phonetic influence of the L2 on the L1 (e.g., Bergmann et al. 2016; Major 1992;

1 For a discussion on the interaction between length of residence and other variables, notably age of acquisition, see Piske et al. (2001). As Piske et al. (2001) note, length of residence "only provides a rough index of overall L2 experience" (p. 197). While many studies talk about immersion, immigration, and length of residence, these may be used as a proxy for overall L2 experience. For the current study, it is relevant that these factors, whether conceptualized as L2 experience or length of residence, function as long-term sources of linguistic change and interaction.

Stoehr et al. 2017). Speakers that had spent longer in the L2 linguistic environment, or those with greater proficiency in the L2, evidenced greater degrees of L2 phonetic transfer to the L1 (e.g., Major 1992), leading Major (1992) to claim that the L1 phonetic system "is not a fixed and stable system but rather a fluid and changeable one that is highly subject to the influence of a well-developed second system" (Major 1992, p. 204). Other research has shown a degree of bidirectional cross-linguistic influence (e.g., Fowler et al. 2008), in which the L1 influences the L2 and the L2 influences the L1. Again, cases of L2 to L1 transfer have most often been found following long-term engagement with the L2.

Although much of this research has focused on a linear trajectory, such as tracking the shift in L2 production towards monolingual norms over time, it is clear that such long-term shifts are dynamic. In their seminal work, Sancier and Fowler (1997) tracked the phonetic production of a single Portuguese-English bilingual speaker as they moved between multiple linguistic environments. Their results showed that, following a stay of several months in a Portuguese linguistic environment, voice onset times (VOTs) for both Portuguese and English productions became shorter and more Portuguese-like, a phenomenon referred to as "gestural drift" (Sancier and Fowler 1997, p. 422). In contrast, following a stay of several months in an English linguistic environment, VOTs for both languages became longer and more English-like. These results demonstrate that a long-term change in linguistic environment can promote a degree of phonetic interaction, whereby both of the bilingual's languages are impacted by the language of the ambient environment. In short, cross-linguistic phonetic influence, as evidenced by bilingual gestural drift, appears to be dynamic and responds long-term to factors in the ambient linguistic environment.

The existing studies on long-term immersion, study abroad, and gestural drift generally consider changes in L1 and L2 phonetic production following a significant length of stay in the L2 linguistic environment, from weeks or months (e.g., Díaz-Campos 2004; Lord 2010; Nagle et al. 2016) to years (Piske et al. 2001). As noted by Simonet (2014), "The vast majority of work on interlingual or cross-linguistic phonetic influence in bilingualism does not explicitly distinguish between long-term and transient interference. Albeit implicitly, most studies explore features that are attributed to long-term interference" (p. 27). Yet more recent research has shown that the degree of cross-linguistic phonetic influence may also be subject to short-term variables.

#### *2.2. Short-Term Sources of Cross-Linguistic Phonetic Influence*

While the previous research detailed above highlights the notion that phonetic influence between a bilingual's two languages may occur as the result long-term factors, more recent research has begun to examine short-term sources of cross-linguistic phonetic influence. As Simonet (2014) notes, other authors use somewhat different terminology to refer to the same or similar phenomenon (p. 27). Grosjean (2012) differentiates between static and transient sources of cross-linguistic influence, while Paradis (1993) differentiates between competence and performance related cross-linguistic interference. For reasons noted in Simonet (2014), the long-term vs. short-term distinction is used henceforth. Here, *short-term* is used in contrast to long-term, broadly referring to situations or contexts in which phonetic production or perception is altered for a single speaker in response to immediate changes in the broader linguistic situation. These short-term factors may include a shift in the languages used in an interaction, also called the language context (e.g., Olson 2016), changes in external sociolinguistic factors, and even use of cognate tokens.

Several studies have begun to examine the potential of the language of a given interaction or experimental session as a source of short-term cross-linguistic phonetic influence. Simonet (2014), for example, examined the production of Catalan vowels by Spanish-Catalan bilinguals across two different sessions: unilingual and bilingual. In the unilingual session, all stimuli—utterances containing the target Catalan tokens—were drawn from Catalan, as were the session instructions. In contrast, stimuli in the bilingual session included utterances containing the target words from both Spanish and Catalan, in random order. It is important to note that the target tokens were embedded in meaningful utterances, and switches between Catalan and Spanish took place only at the utterance level. As such, the tokens under consideration were all non-switched tokens. Results from the production experiment revealed that Catalan vowels differed between the monolingual and bilingual sessions, with Catalan vowels becoming more Spanish-like in the bilingual session. Similarly, in a cued picture-naming paradigm, Olson (2013) compared the VOT of English and Spanish tokens produced in a monolingual session (i.e., 95% English—5% Spanish or 95% Spanish—5% English) with tokens produced in a bilingual session (i.e., 50% English—50% Spanish). Results showed differences in phonetic production of VOT depending on the nature of the session. Non-switched productions in the bilingual contexts, specifically in a participant's L1, shifted in the direction of the opposite language, with English VOTs becoming more Spanish-like and Spanish VOTs becoming more English-like. A similar effect may be seen in perception, in which the auditory context (i.e., English-like or Spanish-like acoustic features) surrounding a given ambiguous token serves to engage a language-specific perceptual system (Gonzales and Lotto 2013; for perceptual boundaries and language modes, see Casillas and Simonet (2018)). Further research has shown that while bilingual experimental sessions may foster cross-linguistic phonetic influence, the nature of this transfer may be dependent on a speaker's proficiency or dominance (Amengual 2018). Notably, Amengual (2018) found that bilinguals were more likely to experience cross-linguistic transfer resulting from a bilingual paradigm in their less dominant language.

These results from experimental paradigms also find some preliminary support from a more naturalistic paradigm. In her study of bilingual English-Arabic speaking children, Khattab (2002) collected naturalistic data in both English and Arabic-oriented language sessions. The results showed that while children clearly differentiated between the two phonetic systems, particularly with respect to /r/, English tokens produced during the Arabic sessions underwent a degree of phonetic transfer, becoming decidedly more Arabic-like. Subsequent analysis suggests that such transfer may relate to the language dominance of the interlocutor (Khattab 2009), with Arabic-accented English used during interactions with Arabic-dominant listeners. These results suggest that the use of two languages in the same interaction may serve to promote a degree of cross-linguistic influence.

Further evidence for cross-linguistic phonetic influence arising from the use of two languages in a single interaction can be seen in work on the phonetics of code-switching. *Code-switching* refers to the alternation between two or more languages or language varieties in a single discourse (e.g., Myers-Scotton 1997). As such, code-switching represents a clear point in an interaction in which both languages are simultaneously (or nearly simultaneously) activated and serves as a potential short-term source of cross-linguistic phonetic influence. Unlike the bilingual sessions detailed above (e.g., Khattab 2002; Olson 2013; Simonet 2014), which varied the ratio of the language used in a given session or block but examined non-switched productions, research focused on the phonetics of code-switching has generally focused on the potential for phonetic transfer at or near the point of switch. A growing body of research has begun to establish that code-switching impacts phonetic production at the segmental level, most notably inducing a degree of phonetic transfer (although, for a lack of transfer, see Grosjean and Miller (1994) and Muldner et al. (2019)). The exact nature of the phonetic influence found has varied. Several studies have found evidence of unidirectional transfer at the point of switch (Antoniou et al. 2011; Balukas and Koops 2015; Bullock et al. 2006), with Language A shifting in the direction of Language B, but Language B failing to show evidence of transfer. Other studies have found bidirectional transfer (Bullock and Toribio 2009; González-López 2012; Olson 2016; Schwartz et al. 2015), with Language A shifting in the direction of Language B and Language B shifting in the direction of Language A (for an account of unidirectional vs. bidirectional transfer, see Olson (2019)). This cross-linguistic phonetic influence has been found across a variety of paradigms, including naturalistic (e.g., Balukas and Koops 2015) and read speech (e.g., Antoniou et al. 2011), and for different types of code-switches (e.g., for single-word insertions, see Olson (2016); for alternational code-switching, see Bullock and Toribio (2009)). These shifts are largely phonetic in nature, rather than phonological, and bilinguals generally do not implement phonological categories of the opposite language. Thus, code-switching, which activates both of a

bilingual's languages in a compressed timeframe, appears to serve as a short-term source for bilingual phonetic influence.

An additional case for short-term phonetic influence driven by linguistic factors can be seen in the production of cross-linguistic cognates. Cognates, words that have significant cross-linguistic overlap in meaning, phonology, and orthography (Amengual 2018), may result in the activation of both language systems, and as such represent a possible short-term source of cross-linguistic influence. Cognates have been shown to be produced with a degree of phonetic transfer. Amengual (2012), for example, showed that Spanish-English bilinguals produced longer (i.e., more English-like) VOTs in Spanish for cognate words than non-cognate words, a finding that held for heritage speakers (i.e., early bilinguals) and both late L1 English—L2 Spanish and L1 Spanish—L2 English bilinguals (for Spanish-Catalan bilinguals, see Amengual (2016)). Again, as cognates may activate both languages, they can be seen as a short-term source of cross-linguistic phonetic influence.

Changes in the language of the paradigm or interaction (e.g., Simonet 2014), code-switching (e.g., Olson 2016), and cognate status represent cases in which a short-term or immediate shift in the linguistic content of an interaction favors a degree of cross-linguistic phonetic influence. Yet, there is some evidence that non-linguistic changes in the external environment may also impact linguistic behavior. Hay and Drager (2010), for example, found that including region-specific objects in the experimental environment, such as a stuffed kangaroo (i.e., Australia-specific) or kiwi (New Zealand-specific), impacted vowel perception. The authors suggest that objects in the "ambient environment" can impact participant phonetic perception (p. 889). Other studies have found that visually salient characteristics of a speaker may influence phonetic perception (e.g., for intelligibility and accentedness, see Babel and Russell (2015)). For example, in a perceptual experiment, Koops et al. (2008) found that listeners' phonetic perception of stimuli reflecting an on-going, age-graded phonetic change (i.e., PIN-PEN unmerger in Houston, TX, USA) depended on perceived speaker age. Paralleling the change in progress in the local community, listeners were more likely to assume merged phonetic categories for older speakers and unmerged categories for younger speakers. The impact of such external factors on phonetic perception also extends to non-visible social information, such as supposed geographic origin (e.g., Niedzielski 1999). In these cases, it is not the linguistic content of an interaction or paradigm that shifts, but rather the surrounding environment and/or perceived interlocutor.

It is worth considering these short-term sources of cross-linguistic influence within the framework of a bilingual's language modes (e.g., Grosjean 1998, 2001, 2008). Bilinguals have the ability to operate along a linguistic continuum from operation entirely in Language A (i.e., monolingual mode) to operation in Language B (i.e., monolingual mode), including a variety of bilingual modes in which each of the two languages may be used to differing degrees. Language mode has been described in terms of the relative activation of each of the bilinguals two languages, with monolingual mode involving the activation of only (or predominantly) one language and a balanced bilingual mode involving the roughly equal activation of the two languages (Grosjean 2008). Grosjean (2008) notes that a speaker's language mode may be impacted by a variety of factors, including the "form and content of the message," the language act, the interlocutors, and the general situation of the interaction. All of these factors may be considered "short-term," in that they are subject to change from day-to-day or even interaction-to-interaction for a single bilingual speaker. Moreover, shifts in a speaker's (or listener's) position on the language mode continuum may impact their language production (or perception) patterns (e.g., Soares and Grosjean 1984). The previous findings of differing levels of cross-linguistic phonetic influence presented above can be reconceptualized as resulting from differing language modes. Short-term sources of cross-linguistic influence can be seen as variables that cause an immediate shift in a bilingual's position along the language mode continuum. Thus, short-term variables, including the language(s) of a given interaction or paradigm, the use of code-switching, and changes in the surrounding environment, may effectively serve to manipulate the relative activation (or suppression) of a bilingual's two languages, with more equal activation resulting in greater degrees of cross-linguistic phonetic influence.

#### *2.3. Research Questions*

Previous research has established that bilinguals, including late L2 learners, establish different phonetic norms for their two languages. Moreover, there appear to be both long-term (e.g., acquisition and immersion) and short-term (e.g., bilingual mode, code-switching) sources of phonetic cross-linguistic interaction. While long-term immersion in a given linguistic environment has been shown to impact the degree of cross-linguistic influence, the current study examines the potential for linguistic environment to act as a short-term source of cross-linguistic phonetic influence. That is, whether a change in linguistic environment results in an immediate shift in the degree of cross-linguistic phonetic influence. Two specific research questions are addressed:

RQ1: Does a short-term change in the linguistic environment impact phonetic production? For this study, a short-term change in linguistic environment is operationalized as a single speaker moving from an English-dominant linguistic environment to a Spanish-dominant linguistic environment (or vice-versa).

**Hypothesis 1.** *Drawing on previous research that has shown an impact of both long-term and short-term sources of cross-linguistic phonetic influence, it was anticipated that a shift in linguistic environment would result in a corresponding shift in phonetic production. Specifically, L1 English—L2 Spanish speakers would produce Spanish tokens with more English-like phonetic features in an English-dominant linguistic environment and more Spanish-like phonetic features in a Spanish-dominant environment.*

RQ2: Does proficiency in the L2 interact with linguistic environment?

**Hypothesis 2.** *Given the previous finding that as L2 experience and proficiency increase, L2 phonetic production shows less evidence of L1 to L2 cross-linguistic influence, it was anticipated that speakers with greater proficiency in the L2 may show smaller e*ff*ects of a change in linguistic environment on their phonetic production.*

To assess the role of linguistic environment on phonetic production, the same participants were tested in both an English-dominant (Indiana, USA) and Spanish-dominant environment (Madrid, Spain). To focus squarely on the potential for linguistic environment to serve as a short-term source of cross-linguistic influence and limit the long-term effects of acquisition and immersion, participants were tested immediately prior to leaving one environment (i.e., less than 72 h pre-departure) and immediately upon arrival in the second environment (i.e., in the first 72 h in the new environment). The order of sessions was counterbalanced, such that one group was tested first in the English-dominant linguistic environment and then in the Spanish-dominant linguistic environment, and the other group received the opposite session order (i.e., Spanish-dominant environment then English-dominant linguistic environment).

Two different levels of phonetic analysis were conducted: an acoustic analysis of the voice onset time cue (VOT) and a global accent rating (GAR). These two measures were chosen to provide both a fine-grained measure of a relevant segment that differs cross-linguistically between English and Spanish (i.e., VOT), as well as a more global measure of perceived accent to capture potential shifts in other features (e.g., vowels, suprasegmental features, etc.) of production (i.e., GAR).

#### **3. Methodology**

#### *3.1. Participants*

Twenty English-speaking (L1) learners of Spanish (L2) participated in the current study. All participants were enrolled in a six-week immersive study abroad program in Madrid, Spain (Spanish-dominant environment) during the summers of either 2015 or 2018. The study abroad program included host family stays, classes at the local university, and several day trips to surrounding cultural sites, all of which were conducted in the L2. Participants were all learners at the intermediate to advanced level, enrolled in 3rd or 4th year university language courses. All participants gave informed

consent prior to beginning the task and the protocol was approved by the Institutional Review Board at Purdue University (Protocol #:<PHONE_NUMBER>).

Immediately prior to the main task, participants completed the Bilingual Language Profile (BLP; Birdsong et al. 2012). The BLP relies on self-assessments of a participant's language history, language use, language proficiency, and language attitudes in each of the relevant languages to provide a composite language score. All participants are native speakers of English, having learned English from birth (*M* age of acquisition = 0.0, *SD* = 0.0) and Spanish after the age of 5 (*M* = 12.1, *SD* = 3.0). Across all components, participants self-rated English as higher than Spanish (Table 1).

**Table 1.** Unweighted results of the Bilingual Language Profile (BLP) subcomponents.

a For each scale, higher ratings correspond to a more engagement with that component of a given language.

Following the BLP scoring procedures (for details, see Birdsong et al. (2012)), a composite language score was computed for each participant in each language, giving equal weight to each subcomponent. The possible composite language score, henceforth referred to as proficiency, ranges from 0, corresponding to no proficiency in the language, to 218, indicating high proficiency in the language. As expected, participants reported high language scores for English (*M* = 204.6, *SD* = 7.9). In contrast, participants reported lower and more varied Spanish language scores (*M* = 77.5, *SD* = 18.4). Figure 1 illustrates the distribution of participants with respect to their Spanish language score. All participants are considered to be English-dominant. All participants reported normal speech and hearing.

**Figure 1.** BLP Spanish language score by participant.

To assess the effect of linguistic environment, but counter-balance session order, participants (*N* = 20) were divided into two mutually exclusive groups. The first group (*n* = 12) was tested first in the English-dominant environment and then in the Spanish-dominant environment. The second group (*n* = 8) was tested first in the Spanish-dominant environment and then in the English-dominant environment. Again, to limit any possible long-term sources of cross-linguistic influence, participants were tested immediately prior to departure from Environment A (< 72 h) and immediately upon arrival in Environment B.2

2 In short, one group of participants was tested en route to the host country of the study abroad program (i.e., prior to leaving Indiana, USA, and upon arrival in Madrid, Spain) and one group of participants was tested en route to the home country (i.e., prior to leaving Madrid, Spain, and upon arrival in Indiana, USA). While this presents a potential confound, such that one group may have had a six-week "advantage" by participating following six weeks of immersion in the host country, statistical analysis controlled for between-participant differences with the random effects structure, effectively comparing each participant to her or himself.

#### *3.2. Stimuli*

Stimuli for the read-aloud task were modified Spanish versions of utterances (*N* = 5) used in a global accent rating task (e.g., Flege 1988; Riney and Flege 1998). Relevant for the VOT analysis, embedded within these utterances were a number of tokens with word-initial voiceless stops. English and Spanish both employ a bi-partite distinction between voiceless and voiced stops in word initial position. VOT, defined as the temporal difference between the release of the oral closure and the onset of vocal fold vibration, has been shown to be a reliable cue to this voicing distinction (e.g., Lisker and Abramson 1964). Although both English and Spanish make use of this phonological distinction between voiceless and voiced phonemes, the phonetic cues differ. Specifically for voiceless stop consonants, English stops are produced with long-lag VOT (30–100 ms), while Spanish is produced with short-lag VOT (0–30 ms). Given this cross-linguistic difference, English-speaking learners of Spanish are tasked with acquiring and maintaining separate the Spanish-like short-lag VOT norms. A number of authors have noted that English-speaking learners of Spanish may produce Spanish voiceless stop consonants with English-like VOTs (e.g., Hammond 2001). Figure 2 shows the spectrogram and waveform for the word <*calle*> [kaݯe] 'street,' produced by a native English speaker (left) and native Spanish speaker (right). While the use of English-like VOT in Spanish voiceless stops is unlikely to cause issues of intelligibility (Lord 2005; Munro and Derwing 1995), it may impact the perception of speaker accentedness. Given both the gradient nature of VOT, and the previous evidence that bilinguals, including L2 learners, are able to effectively distinguish between English and Spanish VOT, VOT may serve as a sensitive measure of cross-linguistic influence between the two phonetic systems. A total of eight words contained word-initial voiceless stops, with the following distribution: /p/ = 2, /t/ = 2, /k/ = 4.

**Figure 2.** Spectrogram and waveform of <*calle*> 'street' produced by a native English speaker (**left**) and a native Spanish speaker (**right**). The difference in VOT is visible in the phoneme /k/.

Relevant for the GAR task, the five utterances contained a wide variety of segments (i.e., consonants and vowels). Several segments were included that differ significantly in English and Spanish phonetic production (e.g., word-initial <r>, which is produced as // in English and /r/ in Spanish), which could potentially serve as markers of English-accented Spanish. In addition, several segments were included that differ across different dialects of Spanish (e.g., <z>, <ci>, and <ce>, are produced as /s/ in most dialects of Spanish, but as /θ/ in Peninsular Spanish), which could potentially serve as markers of the local (Peninsular) dialect of Spanish.

Example (1) provides several sample stimuli. Target phonemes for the VOT analysis are underlined.

#### *3.3. Procedure*

To assess the potential impact of linguistic environment, each speaker participated in two experimental sessions, each conducted in a different linguistic environment. One experimental session was conducted in an English-dominant linguistic environment (Indiana, USA), and one session was conducted in a Spanish-dominant linguistic environment (Madrid, Spain). In Indiana, USA, English is the home language of approximately 91.1% of the population, with Spanish spoken in the home by only 4.7% of the population (U.S. Census Bureau 2018). In Spain, 90% of the population speaks Spanish as a native language, while only 2.2% of the population speaks English as a native language (Instituto Nacional de Estadística 2019), although statistics were not available for the Community of Madrid. Moreover, both Indiana, USA, and Madrid, Spain, are considered to be "single language" environments in which little code-switching is present (Green and Abutalebi 2013). While the experience of each individual participant may vary, a finding captured by the language background questionnaire, the two environments are clearly distinguishable by the language of the broader environment.

Interaction with the experimenter was intentionally conducted in both languages in each session. The experimenter was a native speaker of midwestern American English who had spent several years living in Madrid, Spain and was proficient in the local Spanish dialect. Written instructions, provided before the start of the oral production task, were comprised of both English and Spanish. With the exception of the location of the two sessions, other experimental factors were maintained as equal as possible, using identical equipment, instructions, and consent forms and conducted with the same experimenter.

Following the collection of language background information, target utterances were presented visually using SuperLab v.5 (Cedrus Corporation 2015) and each utterance was repeated three times during each session. Utterances were recorded in a quiet room with a head-mounted microphone using Audacity v. 2.2.2 recording software. Both the instructions and the recording equipment were the same in each of the two different environments.

#### *3.4. Voice Onset Time Analysis*

A total of 960 tokens were considered in the initial VOT analysis (20 speakers × 8 tokens × 3 repetitions × 2 session = 960 tokens). Twenty-five tokens were classified as missing, and an additional 29 tokens were eliminated for a variety of speech errors (i.e., false start on target word) and recording errors (i.e., noisy recording). Lastly, outliers were eliminated (*n* = 5), defined as those tokens with VOT values greater than 3SD above and below the mean. A total of 901 tokens were included in the final VOT analysis.

VOT was defined as the temporal difference between the release of the oral closure and the onset of vibration of the vocal folds (e.g., Lisker and Abramson 1964). Tokens were measured using Praat (Boersma and Weenink 2018), with particular attention to the waveform. Tokens were coded blindly by a trained research assistant who was unaware of the linguistic environment in which the utterance was produced.

Statistical analysis was conducted using R statistical software (R Core Team 2013) and the lme4 package (Bates et al. 2015). Following recommendations by Barr et al. (2013), the maximal random effects structures that permitted model convergence were used. The significance criterion was set at |t| > 2.00. Power analysis was conducted with the simr package (Green and MacLeod 2016).

#### *3.5. Global Accent Rating Analysis*

Five native Spanish speakers from the target region (Madrid, Spain) were recruited as raters for the GAR task. All raters were native speakers of the target dialect, and using the BLP (Birdsong et al. 2012), were considered highly dominant in Spanish. The rating procedure was largely based on work by Riney and Flege (1998).

Two of the three repetitions per utterance were selected from each session for presentation to the native speakers. When possible, preference was given to the second and third repetitions of the stimuli. For productions containing speech errors, such as pauses or fillers, the first repetitions was substituted. Raters could listen to each presentation multiple times, if needed. The intensity of each utterance was scaled via script in Praat (Boersma and Weenink 2018) to 65 dB. The order of presentation was fully randomized. A total of 400 learner-produced utterances were selected for presentation to the native raters.

Raters were asked to provide accent ratings for each utterance on a 9-point Likert scale in which 1 corresponded to a "very strong non-native accent" and 9 a "native accent." Each accent rating was converted to a z-score on a by-rater basis to normalize for the different ranges of values used by each rater. A total of 2000 ratings were provided by native speakers (5 utterances × 2 repetitions × 2 sessions × 20 participants × 5 raters).

Statistical analysis was again conducted using R statistical software (R Core Team 2013) and the lme4 (Bates et al. 2015) and simr (Green and MacLeod 2016) packages. The maximal random effects structures that permitted model convergence were used (Barr et al. 2013).

#### **4. Results**

#### *4.1. Voice Onset Time*

An initial mixed effects model was conducted with VOT (ms) as the dependent variable and linguistic environment (English-dominant environment vs. Spanish-dominant environment) as the fixed effect. Random effects included participant and item (i.e., word), with random slopes and intercepts. Examination of a Q-Q plot confirmed that the residuals of the model were normally distributed. Contrary to the initial hypotheses, results from this initial model demonstrated no significant effect of linguistic environment on VOT, with similar VOTs produced in the English- (*M* = 44.2 ms, *SD* = 19.4 ms) and Spanish-dominant (*M* = 43.6 ms, *SD* = 18.8 ms) environments. The results for the fixed effects are available in Table 2 (for random effects and model equation, see Appendix A). Figure 3 shows the VOTs produced in each linguistic environment, separated by initial phoneme. Again, while we expect some differences in VOT across place of articulation (Cho and Ladefoged 1999), the key comparison is between VOT in the English and Spanish environments.

**Table 2.** Voice Onset Time (VOT) Model Fixed Effects.

**Figure 3.** VOT (ms) by linguistic environment and place of articulation.

To ensure that the lack of a significant effect of linguistic environment on VOT production was not the result of an underpowered study, a power analysis was conducted using the simr package (Green and MacLeod 2016). Results of a simulation-based power analysis, with a medium effect size (*d* = 0.5) and based on 500 simulations, showed that the current study design surpassed the 80% power threshold (power for predictor linguistic environment = 99.8%, CI = [98.9, 100]). The outcome of the power analysis suggests that the lack of a significant effect of linguistic environment is not likely to be the result of an underpowered study design.

Related to the second research question, namely whether the effect of linguistic environment is conditioned by a given participant's proficiency in the target language, a second mixed effects model was conducted with the dependent variable of VOT. Fixed effects included linguistic environment (English-dominant environment vs. Spanish-dominant environment), proficiency, and their interaction. Proficiency was included as a continuous variable, with proficiency values determined by each participant's overall BLP language score for Spanish (Birdsong et al. 2012).3 Random effects included participant and item, with random intercepts and random slopes by linguistic environment. More complex random effects structures, specifically random slopes by each of the two fixed effects, did not permit model convergence. Examination of a Q-Q plot confirmed that the residuals of the model were normally distributed. Results of this second model showed (see Table 3) that there was no significant effect of either linguistic environment or proficiency on VOT production. Moreover, there was no significant interaction between these two fixed effects, suggesting that linguistic environment was not a factor, regardless of a given participant's level of proficiency (for random effects and model equation, see Appendix B). Figure 4 illustrates these results. Again, lower VOT corresponds to more native-like pronunciation. Note that, while proficiency was included as a linear predictor in the model, Figure 4 grouped participants by relative proficiency for the purposes of visualization. Worth noting, the general expected trend is visible in Figure 4, with participants with higher proficiency in Spanish showing lower, more Spanish-like VOTs. However, it is clear that there is no effect of linguistic environment.4

3 Following suggestions by an anonymous reviewer, subsequent analysis was conducted with proficiency as a three-way categorical variable. Two different group cut-offs were considered. First, parallel to the subgroups in Figure 4, three approximately equal-sized proficiency groups were considered: low (n = 6), mid (n = 7), and high proficiency (n = 7). Second, three proficiency groups were identified using visual analysis of participant BLP Spanish score distributions: low (n = 5), mid (n = 12), and high proficiency (n = 3). Model comparison, following the procedure outlined below, showed that neither categorical approach to proficiency significantly improved model fit for either the VOT (equal sized groups: χ2(4) = 1.380, *p* = 0.848; unequal sized groups: χ2(4) = 7.427, *p* = 0.115) or the GAR analysis (equal sized groups: χ2(4) = 3.517, *p* = 0.475; unequal sized groups: χ2(4) = 5.690, *p* = 0.224) relative to a model without proficiency. As such, proficiency, regardless of operationalization, does not appear to significantly influence VOT or interaction with linguistic environment for this group of learners.

4 As the main goal of this project was to examine the effect of linguistic environment, the main analysis compares the productions of participants in two different linguistic environments. The two groups (i.e., US-Spain and Spain-US) served to counter-balance session order. As such, some group differences are possible as the Spain-US group was tested following six weeks in the Spanish linguistic environment. Addressing the possible effect of group, a subsequent model was conducted with linguistic environment and group as fixed effects,. Model comparison showed that the inclusion of group significantly improved model fit (χ2(2) = 7.395, *p* = 0.025). This is not unexpected, given that overall, the Spain-US group (*M* = 39.6 ms, *SD* = 18.3 ms) produced significantly shorter VOTs than the US-Spain group (*M* = 46.8 ms, *SD* = 19.1 ms), *t*(787) = 5.693, *p* < 0.001. To confirm that the impact of linguistic environment was similar for each group, separate models were conducted for each group. The model structure was parallel to the main model above. Results suggested that linguistic environment did not significantly impact VOT for either the US-Spain group (*b* = −2.616, *t* = −1.136) or the Spain-US group (*b* = 4.799, *t* = 1.923).

**Figure 4.** VOT (ms) by linguistic environment and proficiency. For the purposes of illustration only, participants were grouped into low (*n* = 6), mid (*n* = 7), and high proficiency (*n* = 7) groups by the continuous Spanish language component score of the BLP.

Finally, for completeness, a model comparison was done to assess the contribution of each of the variables of interest. Model comparison was conducted by comparing the model involving the two fixed effects (i.e., linguistic environment and proficiency), with the random effects structure previously detailed above, to submodels created by dropping one of the fixed effects but maintaining a similar random effects structure. Results of the model comparison show that there was no significant difference between the most complex model (log likelihood = −3706.4) and the submodel without the fixed effect of linguistic environment (log likelihood = −3706.4, χ2(2) = 0.041, p = 0.979). Similarly, there was no difference between the most complex model and the submodel without the fixed effect of proficiency (log likelihood = −3707.2, χ2(2) = 1.700, *p* = 0.428). As such, neither fixed effect contributed significantly to improving the model fit.

Taken as a whole, the results of the VOT analysis suggest that linguistic environment does not significantly impact the production of VOT. Moreover, for this group of learners, proficiency does not seem to be a relevant factor in the production of VOT.

#### *4.2. Global Accent Rating*

While the VOT analysis focuses on a specific segment, limited to only the voiceless stop consonants, the GAR analysis provides a more holistic metric of participant phonetic production. That is, while linguistic environment may not play a role in the production specifically of VOT, it is possible that other phonetic components, relevant to and noticeable by native speakers, may be modulated by environment.

Again, an initial mixed effects model was conducted with z-scored accent ratings as the dependent variable and linguistic environment as the fixed effect. Random effects included participant and item (i.e., utterance), with random slopes and intercepts. More complex random effects structures, particularly including rater as a random effect, did not permit model convergence. Again, each rating from the 9-point Likert scale was converted into a z-score on a by-rater basis. A visual analysis of the Q-Q plot confirmed that the residuals of the model were normally distributed. Results from this initial model closely parallel the results from the VOT analysis. Specifically, there was no significant impact of linguistic environment on accent ratings, with accent ratings for utterances produced in the English-dominant linguistic environment (*M* = -0.027, *SD* = 1.004) similar to those produced in the Spanish-dominant linguistic environment (*M* = 0.038, *SD* = 1.001). Full fixed effects results are seen in Table 4 (for random effects and model equation, see Appendix C).5 Figure 5 illustrates the

5 Parallel to the by-group analysis for VOT, mixed effect model was conducted on GAR with linguistic environment and group as fixed effects. Model comparison showed that the inclusion of group did not significantly improve model fit (χ2(2) = 5.143,

global accent ratings by linguistic environment. Again, a higher accent rating corresponds to a more native-like production.

**Table 4.** Global Accent Rating Model Fixed Effects.

**Figure 5.** Global accent rating (z-scored) by linguistic environment.

To confirm that the lack of effect of linguistic environment on GAR was not due to an underpowered study, a power analysis was conducted using a simulation based-approach with the simr package (Green and MacLeod 2016). Results, based on 500 simulations with a medium effect size (*d* = 0.5), showed that the experiment exceeded the 80% threshold (power for predictor linguistic environment = 99.2%, CI = [97.9, 99.8]).

Considering the role of proficiency, a second model was conducted with linguistic environment and proficiency, as well as their interactions, as fixed effects. Participant and utterance were included as random effects, with random intercepts and slopes by linguistic environment, which was the maximal effects structure that permitted model convergence. Examination of a Q-Q plot confirmed that the residuals of the model were normally distributed. Results from the model with proficiency (for fixed effects, see Table 5; for random effects and model equation, see Appendix D) showed no significant effect of either linguistic environment or proficiency, and no significant interaction between the two fixed effects. Worth noting, the effect of proficiency trended in the expected direction, with participants who had higher self-rated Spanish language skills being rated as having more native-like accents.

Finally, a model comparison was conducted to examine the contribution of each of these fixed effects. The most complex model, with linguistic environment and proficiency as fixed effects, was compared to submodels created by dropping one of the fixed effects but maintaining the same random effects structure. Results from the model comparison showed that there was no significant

*p* = 0.076). As with VOT, results demonstrated that linguistic environment did not significantly impact the GAR for either the US-Spain group (*b* = 0.13, *t* = 1.169) or the Spain-US group (*b* = −0.03, *t* = −0.572).

difference between the complex model (log likelihood = −2407.7) and either the submodel without linguistic environment (log likelihood = −2408.0, χ2(2) = 0.7496, *p* = 0.687) or the submodel without proficiency (log likelihood = −2409.3.0, χ2(2) = 3.315, *p* = 0.191).

As with the analysis for VOT, the analysis from the GAR data suggests that linguistic environment did not impact native speaker ratings of learner productions. Moreover, while proficiency trended in the expected direction, it did not significantly interact with linguistic environment.

#### **5. Discussion**

The findings of this study add to the discussion on short-term sources of cross-linguistic influence and interaction. With respect to the first research question, and contrary to the initial hypothesis, the results showed that there was no significant effect of linguistic environment on cross-linguistic phonetic influence. The language of the broader community where an interaction took place had no relation to a bilingual's phonetic production. This result was found at both the fine-grained phonetic level, through an analysis of the VOT associated with Spanish voiceless stop consonants, as well as the more global level, as shown by the native speaker global accent ratings. Both VOT and perceived accent did not differ based on the linguistic environment of the session. Power analyses suggested that this lack of significant results is unlikely to be attributed to an underpowered study.

With respect to the second research question, namely the potential role of proficiency in modulating cross-linguistic phonetic influence, the results showed no significant role of proficiency for this particular group of speakers, and importantly, no interaction with the variable of linguistic environment. For all participants, regardless of proficiency, linguistic environment did not play a significant role in determining the degree of cross-linguistic influence or transfer. Again, this finding is contrary to the initial hypotheses.

The first hypothesis, specifically that linguistic environment would impact phonetic production, was driven by a robust body of research that has shown that bilingual phonetic production, and the degree of cross-linguistic phonetic interaction, is impacted by both long-term and short-term factors. Directly related to this hypothesis, we have seen that long-term immersion, either through immigration (Piske et al. 2001), travel (Sancier and Fowler 1997), or study abroad (Casillas 2020; Lord 2010; Solon and Long 2018), impacts phonetic production. Broadly, this research has shown that, over time, both a speaker's L2 and L1 (Bergmann et al. 2016; Major 1992; Stoehr et al. 2017) shift in the direction of the language of the broader community. Considering short-term sources of cross-linguistic phonetic influence, previous work has highlighted several short-term sources, including the language of a given interaction (e.g., Amengual 2018; Olson 2013; Simonet 2014), the use of code-switching (Antoniou et al. 2011; Balukas and Koops 2015; Bullock et al. 2006; Olson 2016), the presence of salient region-specific extra-linguistic cues in the interactional environment (Hay and Drager 2010), and visible (e.g., Babel and Russell 2015; Koops et al. 2008) and non-visible (Niedzielski 1999) social information about an interlocutor. Considering this line of research, it was anticipated that linguistic environment would impact production in the short-term, with phonetic targets shifting in the direction of the broader linguistic environment. Namely, it was anticipated that tokens produced in Madrid, Spain, would become more Spanish-like and tokens produced in Indiana, USA, would become more English-like. Given the lack of support for this hypothesis, it is worth considering several possible explanations.

One possible explanation for the lack of a short-term impact of linguistic environment is that the immediate local context of an interaction may be more relevant than the broader environment in which an interaction takes place. In much of the previous research on short-term sources of cross-linguistic phonetic influence, the source of the influence is present either in the interaction itself, either real (i.e., the language(s) required by the paradigm (e.g., Simonet 2014)) or imagined (i.e., visible or non-visible sociolinguistic cues (e.g., Babel and Russell 2015)), or is present in the physical environment that immediately surrounds participants (i.e., region-specific cues in the experimental setting (Hay and Drager 2010)). In each of these cases, the source of the short-term phonetic influence is in the speaker's immediate context. As such, the findings of the current study suggest a possible primacy of this immediate context relative to the broader linguistic environment. In short, the immediate context of the interaction is more relevant for phonetic production and perception than the broader linguistic environment, and short-term sources of cross-linguistic phonetic influence are local, rather than global. In the current study, the immediate context was maintained as similar as possible across the two experimental sessions. The same experimenter greeted participants, and the language of both the interaction with the experimenter (i.e., bilingual/code-switched interaction) and the written instructions were the same in both sessions. If there exists a primacy of the immediate context for short-term sources of cross-linguistic phonetic influence, these local characteristics of the interaction may have been more relevant than the linguistic environment of the broader community.

A second possible explanation for the lack of an impact of linguistic environment on phonetic production in the current study is related to the study population. While previous research has shown that L2-learners' phonetic productions are impacted by both long-term and short-term sources of cross-linguistic phonetic influence, the participants in the current study have relatively low proficiency in the L2. Directly related to their phonetic systems, the mean VOT values produced by these participants (for all productions *M* = 43.9 ms, *SD* = 19.1 ms) remain well outside the norms for native speakers (e.g., Lisker and Abramson 1964) and both early and late bilinguals (Amengual 2012). As such, it is possible that these participants are not sufficiently proficient in the L2 to respond to short-term sources of cross-linguistic influence at the phonetic level. The possible role of proficiency as a mitigating factor is echoed in previous work on L2 development during long-term engagement with a given language (e.g., study abroad), in which lower-proficiency speakers evidence less change in the L2 than higher-proficiency speakers during the immersion experience (for discussion of a threshold hypothesis in study abroad, see Lafford and Collentine (2006)). As such, a speaker's proficiency level may serve to modulate the impacts of short-term sources and effectively limit the role of linguistic environment in the current population. Moreover, the population for this study was fairly homogenous at the phonetic level, as illustrated by the minimal differences in VOT between the highest and lowest proficiency groups (see Figure 4; mean difference = 6.1 ms). This degree of homogeneity may further explain the lack of an impact of proficiency and the failure to support the second hypothesis.

Finally, it is worth returning to the language mode framework to provide an account for the role of both long-term and short-term sources of cross-linguistic influence. Grosjean (2001) provides a variety of factors that influence language mode: the interlocutors, the situation and physical location, the function of the language act, the type of stimuli and task, etc. It is worth noting that all of these factors can be considered as short-term sources of cross-linguistic influence and are subject to change within and between different interactions. Grosjean (2001) notes that language mode "concerns the level of activation of two languages" (p. 42), and the short-term variables may be the primary drivers of shifts in language mode. In contrast, the long-term sources of linguistic interaction, including acquisition, changes in proficiency, and immersion, are not listed as factors that impact language mode. As language mode has been described as a continuum, from monolingual operation in Language A to monolingual operation in Language B, short-term factors may serve to adjust a participant's position along their existing continuum. Long-term factors, such as proficiency, may serve to manipulate the nature of the endpoints of this continuum. Additional support for this interpretation comes from work in bilingual lexical access, notably from picture-naming tasks. This line of work has shown that short-term factors, such as the ratio of one language to another, impacts lexical access in both production (e.g., Gollan and Ferreira 2009; Olson 2015) and perception (e.g., Olson 2017), but that these effects are modulated by more long-term oriented factors like proficiency and language dominance (e.g., Olson 2015; Schwieter and Sunderman 2008). In terms of activation, long-term factors may ultimately manipulate a given language's baseline activation level or range of possible activation, while short-term factors manipulate the comparative level of activations of the two languages within their possible ranges.

#### *Future Directions*

Future research should continue to systematically examine the differential impacts of both longand short-term sources of cross-linguistic phonetic influence. Building directly on the current study, and particularly in light of the failure of the results to support the initial hypotheses, future research may seek to expand upon the current study with a more heterogeneous population. Of particular interest may be to examine participants across a wide range of proficiencies, from learners to highly proficient early bilinguals, as they move through different linguistic environments. Moreover, developing a better understanding of other individual factors, such as participant's engagement with the local context (i.e., whether they maintain significant use of the L1 or immediately begin to engage in the L2), may serve to further our understanding of variability in cross-linguistic phonetic interaction. Second, it is acknowledged that the data set for the acoustic analysis is limited, both in terms of the number of tokens and the variety of features examined. Future research may seek to replicate these findings with a larger data set and a variety of phonetic features. For example, a more robust analysis across different places of articulation, precluded here by the size of the data set, may also be of interest. Notably, there appears to be some slight advantage for /t/ relative to the other places of articulation (see Figure 3), which would suggest that different phonemes may undergo different levels of cross-linguistic influence (for a discussion of potential differences in the perceptual prominence of voiceless stops by place of articulation, see Ruch and Peters (2016, p. 28)). Furthermore, research may seek to disentangle the possible effects of the immediate interactional context and the broader environment of that context, exploring the possible notion of a primacy of immediate or local factors as short-term sources of cross-linguistic influence.

#### **6. Conclusions**

This study examined the potential for linguistic environment, conceptualized as the language norms of the broader community in which an interaction or experiment takes place, to serve as a short-term source of cross-linguistic influence. To assess the role of linguistic environment, bilinguals (i.e., English speaking learners of Spanish) produced Spanish utterances in two sessions: an English-dominant linguistic environment (Indiana, USA) and a Spanish-dominant linguistic environment (Madrid, Spain). Productions were analyzed at the fine-grained acoustic level, though an acoustic analysis of voice onset time, as well as more holistically through native speaker global accent ratings. Results showed that linguistic environment did not significantly impact either measure of phonetic production. Moreover, there was no interaction of proficiency with linguistic environment, suggesting that the linguistic environment was not a relevant factor, regardless of participant proficiency in their second language.

The current findings, notably the lack of an impact of the broader linguistic environment in determining cross-linguistic phonetic influence, may suggest a primacy of the local factors (i.e., characteristics of the interaction and the immediately surrounding area) over broader, global factors (i.e., the linguistic environment of the broader community surrounding the interaction) as sources of short-term cross-linguistic interaction. Further research is needed to confirm these results and continue to explore the role of both long-term and short-term sources of cross-linguistic phonetic influence.

**Funding:** This research was funded in part by the Purdue Research Foundation.

**Acknowledgments:** I am grateful for the technical work of Serae Neidigh on this project. All errors are my own.

**Conflicts of Interest:** The author declares no conflict of interest. The funding sponsors had no role in the design of the study; in the data collection, analyses, or interpretation of the data; in the writing of the manuscript, or in the decision to publish the results.

#### **Appendix A**

**Table A1.** Voice Onset Time Model Random Effects.

Equation:

$$VOT\_{ijk} = \beta\_0 + \beta\_1 \ast I \text{(Enviromement}\_{ijk} = B\text{)} + \text{Particinant}\_{\rangle} + \text{Item}\_k + \epsilon\_{ijk}$$

#### **Appendix B**

**Table A2.** Voice Onset Time Model with Proficiency Random Effects.

Equation:

$$\begin{array}{lcl} \mathit{VOT}\_{ijk} &=& \beta\_0 + \beta\_1 \ast \mathrm{I}\{\mathit{Env}\,\text{romment}\_{ijk} = B\} + \beta\_2 \ast \mathit{Projiction} + \beta\_3\\ &\ast \mathrm{I}\{\mathit{Env}\,\text{romment}\_{ijk} = B\} \ast \mathrm{Projiction} + \gamma\_{\rangle} \ast \mathrm{I}\{\mathit{Env}\,\text{romment}\_{ijk} = B\} \\ &=& B\rangle + \eta\_k \ast \mathrm{I}\{\mathit{Env}\,\text{romment}\_{ijk} = B\} + \mathrm{Partif}\{\mathit{runnt}\_{\rangle} + \mathrm{I}\{\mathit{Env}\_k + \epsilon\_{ijk}\}\} \end{array}$$

#### **Appendix C**

**Table A3.** Global Accent Rating Model Random Effects.

Equation:

$$GAR\_{ijk} = \beta\_0 + \beta\_1 \circ l \\
\text{(Enviromement}\_{ijk} = \, ^\circ B\text{)} + \text{Particinant}\_{j} + \text{Item}\_k + \epsilon\_{ijk}$$

#### **Appendix D**

**Table A4.** Global Accent Rating Model with Proficiency Random Effects.

Equation:

$$\begin{array}{lcl} \text{GAR}\_{ijk} &= \beta\_0 + \beta\_1 \ast \text{I}\{\text{Enviornment}\_{ijk} = B\} + \beta\_2 \ast \text{Projiciency} + \beta\_3\\ &\ast \text{I}\{\text{Enviornment}\_{ijk} = B\} \ast \text{Projciency} + \gamma\_j \ast \text{I}\{\text{Enviornment}\_{ijk}\}\\ &= B\rangle + \eta\_k \ast \text{I}\{\text{Enviornment}\_{ijk} = B\} + \text{Particpent}\_k + \text{Item}\_k + \epsilon\_{ijk} \end{array}$$

#### **References**

Amengual, Mark. 2012. Interlingual influence in bilingual speech: Cognate status effect in a continuum of bilingualism. *Bilingualism: Language and Cognition* 15: 517–30. [CrossRef]

Jarvis, Scott, and Aneta Pavlenko. 2008. *Crosslinguistic Influence in Language and Cognition*. New York: Routledge.

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## *Article* **Does Teaching Your Native Language Abroad Increase L1 Attrition of Speech? The Case of Spaniards in the United Kingdom**

#### **Robert Mayr 1,\*, David Sánchez 1 and Ineke Mennen 2**

Received: 30 August 2020; Accepted: 16 October 2020; Published: 22 October 2020

**Abstract:** The present study examines the perceived L1 accent of two groups of native Spaniards in the United Kingdom, Spanish teachers, and non-teachers, alongside monolingual controls in Spain. While the bilingual groups were carefully matched on a range of background variables, the teachers used Spanish significantly more at work where they constantly need to co-activate it alongside English. This allowed us to test the relative effect of reduced L1 use and dual language activation in first language attrition directly. To obtain global accentedness ratings, monolingual native Spanish listeners living in Spain participated in an online perception experiment in which they rated short speech samples extracted from a picture-based narrative produced by each speaker in terms of their perceived nativeness, and indicated which features they associated with non-nativeness. The results revealed significantly greater foreign-accent ratings for teachers than non-teachers and monolinguals, but no difference between the latter two. Non-native speech was associated with a range of segmental and suprasegmental features. These results suggest that language teachers who teach their L1 in an L2-speaking environment may be particularly prone to L1 attrition since they need to co-activate both their languages in professional settings and are regularly exposed to non-native speech from L2 learners.

**Keywords:** L1 attrition; speech; foreign accent; accent perception; Spanish; English; bilingual; teacher

#### **1. Introduction**

A growing body of research on speech development in early and late bilinguals has documented changes occurring in a speaker's first language (L1) pronunciation that affect areas, such as vowels, consonants, and intonation patterns (e.g., (de Leeuw 2019; Fowler et al. 2008; Mayr et al. 2019; Mennen 2004; Nodari et al. 2019)). Such changes can take place rapidly, affect novice second language (L2) learners (Chang 2012, 2013; Kartushina et al. 2016a), and may be fully (Kartushina and Martin 2019) or partially (Chang 2019) reversed. Alternatively, they may occur over time in proficient L2 learners who are long-term residents in an L2-speaking environment (e.g., (de Leeuw et al. 2018a; Dmitrieva 2019; Mayr et al. 2012; Ulbrich and Ordin 2014)). Only the latter scenario is usually referred to as "L1 attrition", that is, the non-pathological and non-age related decrease in an individual's proficiency in a previously learnt language ((Köpke and Schmid 2004; Schmid 2010), but see (Schmid and Köpke 2017) for a broader definition).

While L1 attrition of speech has been widely documented, not all individuals who are long-term residents in an L2-speaking environment exhibit observable changes to their native accent (de Leeuw et al. 2018a; Major 1992; Mennen 2004). The specific factors that facilitate or hinder attrition of speech are, however, still poorly understood, and few studies have systematically investigated relevant predictor variables (but see (Hopp and Schmid 2013)). One of the ongoing debates in this context is whether attrition is predominantly caused by reduced L1 use or by cross-linguistic interactions arising from contexts of dual language activation (de Leeuw et al. 2010; Schmid 2007; Stoehr et al. 2017). The present study aims to contribute to this issue by examining the perceived L1 accent of two groups of native Spanish speakers in the United Kingdom: (1) Spanish language teachers, who use their L1 regularly in professional settings and frequently need to switch between Spanish and English, and (2) non-teachers, who virtually never use the L1 in the workplace, with the two groups exhibiting similar use of the L1 in social situations. This design allowed us to test the role of low L1 use and regular dual language activation in L1 attrition of speech directly.

#### *1.1. Plasticity in Native Speech, Phonetic Drift and L1 Attrition*

Research into late bilingualism has until recently been primarily concerned with L2 acquisition where prevailing notions have been that a critical period (Lenneberg 1967) and processes of fossilization (Selinker 1972) constrain ultimate attainment in the L2. Whether this putative end state is maturationally constrained or conditioned by increasing entrenchment is still subject to ongoing debates (e.g., (Bylund et al. 2013; Piske et al. 2001); nonetheless, traditional perspectives on bilingualism have largely ignored the L1, assuming it to be stable and unlikely to undergo significant development (e.g., (Gregg 2010)).

Such suggestions, however, are not supported by empirical findings which show that bilinguals' L1 speech patterns typically differ from those of monolinguals (see (Kartushina et al. 2016b) for an overview, and below). Moreover, they are at odds with a holistic view of bilingualism, which argues that the L1 and L2 do not exist in isolation but constantly interact with each other (Grosjean 1989). In line with this account, the Speech Learning Model (SLM) (Flege 1995; Flege and Bohn 2020) posits that the L1 and L2 share a common phonological space and influence each other, which may lead to cross-linguistic assimilation and dissimilation patterns, both of which will differ from those of monolinguals. Moreover, the SLM claims that the more experienced an L2 learner is, the greater the effect of the L2 will be on the L1 (Flege 1995).

A static view of the L1 has also been challenged by the advocates of Dynamic Systems Theory (e.g., (de Bot et al. 2007)). According to this account, language constitutes a system with multiple components that are continually in a state of flux. These components are interconnected and sensitive to feedback, both from internal stimuli (i.e., other components within the system), and social and environmental factors. Thus, native speech patterns are dynamic and subject to change throughout the lifespan. Indeed, there is widespread evidence from longitudinal studies that show that even monolinguals modify their L1 accent in response to changes in the norms of their speech community (Harrington 2006; Harrington et al. 2000; Sankoff and Blondeau 2007). Amongst these, a particularly well-known example is the work of Harrington and his associates which showed systematic changes over several decades in the Queen's vowel realizations during her annual Christmas address.

Changes in L1 accent have also been widely documented in longitudinal work on bilinguals. For example, Sancier and Fowler (1997) present the case study of a Brazilian Portuguese-English bilingual who regularly travelled between Brazil and the United States (see also (Tobin et al. 2017) for a recent extension to Spanish-English bilinguals). They found that her voice onset time (VOT) values in both languages were longer after several months in the United States and shorter after months in Brazil, a change to which native Portuguese listeners were receptive. The authors ascribe the observed variation to what they call a "gestural drift" (more recently "phonetic drift" (Chang 2012, 2013), suggesting that L1 phones begin to adopt characteristics of the ambient language as a result of their similarity to L2 phones, the speakers' propensity to unintentionally imitate what they hear, and the effect of recency on memory. Since phonetic drifts of this kind do not coincide with a decline in L1 proficiency, they are not considered instances of attrition (see also (Chang 2012, 2013; Kartushina et al. 2016a)).

In contrast, an extensive body of literature has documented pervasive changes in L1 accent in bilinguals who are long-term residents in an L2-speaking environment. At the phonetic level, such instances of L1 attrition have been shown to affect the production of VOT in plosives (Flege 1987; Major 1992; Mayr et al. 2012; Stoehr et al. 2017), formant frequencies in vowels (Bergmann et al. 2016; Guion 2003; Mayr et al. 2012), laterals (de Leeuw et al. 2013; de Leeuw 2019) and rhotics (de Leeuw et al. 2018b; Ulbrich and Ordin 2014), and the realization of tonal alignment (de Leeuw et al. 2012; Mennen 2004). Attrition has also been shown to affect L1 perception (Ahn et al. 2017; Dmitrieva 2019) and may result in the neutralization of native phonological contrasts (Cho and Lee 2016; de Leeuw et al. 2018a).

Moreover, there is ample evidence that listeners are receptive to changes in L1 accent and may perceive speakers as foreign accented in their native language (Bergmann et al. 2016; de Leeuw et al. 2010; Hopp and Schmid 2013). For example, de de Leeuw et al. (2010) examined the global foreign accent in the L1 of native German speakers who were long-term residents in Anglophone Canada or the Netherlands. The results revealed that they were perceived to be significantly less native-like than native control speakers in Germany, irrespective of geographical setting. Similarly, the native German speakers in Anglophone North America in Bergmann et al. (2016) were perceived as significantly less native-like in their L1 than control speakers in Germany, with 40% of attriters rated below the monolingual range.

Together, the extant literature hence suggests that L1 attrition of speech is widespread and may be observed both in the productions of bilinguals and in their global foreign accent ratings. Nevertheless, attrition is not inevitable since not all individuals who are long-term residents in an L2-speaking environment end up with changes to their L1 accent. For example, de Leeuw et al. (2018a) showed that while one of their Albanian-English bilinguals completely neutralized the L1 phonemic contrast between light and dark laterals, and two additional ones did so only in coda position, others produced their laterals entirely like Albanian monolinguals. Similarly, in Mennen's (2004) study of tonal alignment, four out of five of her Dutch learners of Greek exhibited changes in their L1 alignment patterns, but one speaker did not, producing tonal alignment entirely natively in both languages (see also (de Leeuw et al. 2013; Major 1992)). Finally, instances of individual variation were found in studies of accent perception (Bergmann et al. 2016; de Leeuw et al. 2010). Thus, while 14 bilinguals in de Leeuw et al. (2010) received a clear non-native rating, 20 were consistently perceived as native.

#### *1.2. L1 Use and Dual Language Activation in L1 Attrition*

One of the variables that may account for such individual variation in L1 attrition of speech is language use. For example, Flege et al. (1997) showed that Italians in the United States had stronger foreign accents in L2 English if they used Italian a lot than if they used it rarely. Similarly, Lloyd-Smith et al. (2020) found a strong effect of Italian use scores on the perceived nativeness in Italian heritage speakers in Germany, while the age at which the heritage language was introduced was inconsequential. Stangen et al. (2015), in turn, found high non-native accents in the majority language German for Turkish heritage language speakers in Germany with high use of Turkish (see also (Kupisch et al. 2014)).

Similar effects of language use have also been documented in attrition contexts. Thus, Stoehr et al. (2017) examined VOT production in two groups of late Dutch-German bilinguals living in the Netherlands, L1 German speakers and L1 Dutch speakers. Native German speakers were exposed to their L1 only at home, whilst speaking Dutch in other environments, whereas the native Dutch speakers had more contact with their L1 given its status as the majority language, only coming into contact with L2 German at home. The study found that L2-immersed bilinguals produced nativelike L2 plosives, yet also exhibited L2-like characteristics in their L1 productions. Conversely, bilinguals living in the L1 environment did not produce nativelike L2 plosives but maintained nativelike L1 VOTs. Together, the results suggest that being immersed in an L2-speaking environment can be advantageous for L2 speech learning, but reduced L1 use may increase the likelihood of L1 attrition.

The idea that low L1 use should lead to attrition is based on the premise, consistent with exemplar theoretic and usage-based approaches, that language use reinforces memory representations, and that its absence may lead to retrieval difficulties (Bybee 2001). Nevertheless, the role of L1 use in attrition is not straightforward. First, a number of studies have shown that changes to L1 accents can occur despite continued high L1 use (Chang 2012; Mayr et al. 2012; Mennen 2004). For instance, Mayr et al. (2012), who investigated L1 attrition of speech in Dutch-English twin sisters, documented changes in L1 accent in the L2-immersed twin despite regular high use of her native Dutch. Mennen (2004), in turn, showed in her study of Dutch-Greek bilinguals in the Netherlands that L1 phonetic changes can even occur in an L1-speaking environment provided the frequency of L2 use is high. Second, L1 use and exposure must be seen as distinct from L2 immersion, in that residence in an L2-speaking environment can co-occur with wide and varied patterns of L1 communication. As such, simple measures of frequency and quantity of L1 contact may not be sufficient, since "[ ... ] among bilinguals, L1 use does not necessarily equal L1 use" (Schmid 2007, p. 137). That is to say, L1 use encompasses a diverse range of situations that do not fit comfortably within a single definition, and therefore cannot be considered a single predictor of attrition.

One of these concerns situations that require co-activation of the L1 and L2. Thus, in de Leeuw et al.'s (2010) study, native German speakers in Anglophone Canada and the Netherlands were more likely to be perceived as foreign-accented in the L1 if they used German in contexts in which code-switching was likely to occur. Bilinguals who reported a high amount of L1 contact in situations with minimal expected code-switching, on the other hand, were less likely to be perceived as non-native, suggesting that L1 contact of this type may promote stability of pronunciation. Note, however, that in this study, participants were not directly asked whether they code-switched in specific settings. Rather, the authors postulated ex post facto that code-switching was more likely to occur in certain settings. These included L1 use with family members and friends in Canada and the Netherlands and use in church settings; in contrast, code-switching was deemed less likely to occur in work settings, during visits to Germany, and during telephone conversations and written correspondence with native German speakers.

These findings are consistent with a large body of evidence that has shown cross-linguistic interactions to occur in contexts of dual language activation, such as code-switching, where inhibition of the non-target language is particularly difficult (Green 1998). The state of activation of a bilingual's two languages at a given point in time is referred to as language mode (Grosjean 2001) and can range from bilingual mode, where both languages are fully activated, to monolingual mode, where the non-target language is inhibited as much as possible, although never entirely, based on sociolinguistic factors. Studies of phonetic code-switching have shown unidirectional interactions, in which the speech patterns of only one language are affected by those of the other one (e.g., (Muldner et al. 2019; Olson 2013)) as well as bidirectional interactions, in which both languages mutually affect each other's speech patterns (e.g., (Bullock and Toribio 2009; Piccinini and Arvaniti 2015)), with few studies revealing no effect of switching (but see (Grosjean and Miller 1994)).

#### *1.3. The Present Study*

The present study sought to build on previous work that has examined the role of L1 use and dual language activation in L1 attrition by investigating the perceived L1 accent of two groups of native Spanish speakers in the United Kingdom, (1) Spanish language teachers, and (2) non-teachers, alongside monolingual controls in Spain. As such, it is the first to examine L1 attrition of speech across specific professional groups. To the best of our knowledge, only one other study on L1 speech production has included individuals who teach their native language in an L2-speaking environment, that is, Chang (2019). However, unlike the present study, the speech of the L1 English speakers in that study, who taught their native language to L2 learners in Korea, was not compared to that of a group of non-teachers. Moreover, the focus of that study was the effect of bilinguals' L2 use on L1 pronunciation patterns.

The case of teachers is particularly pertinent, given the high proportion of foreign citizens who work teaching their native languages: Of an estimated 116,000 Spaniards in the United Kingdom between 2013 and 2015, nearly 10% were working in education (Office for National Statistics 2017). While other migrants may also have frequent L1 contact, the experience of language teachers is quite distinct, given their high levels of L1 exposure and use under specific circumstances. Thus, language teaching is one of the few professions in which language is not merely a medium of communication, but also its object. As such, individuals who teach their native language to L2 learners may have what Chang (2019, p. 108) refers to as an "instructional orientation" towards the L1, which would typically encompass "high metalinguistic awareness and explicit knowledge of rules, norms, and standards" (ibid.). Moreover, the need for them to provide a clear, carefully articulated model for their students' pronunciation patterns means that they may be particularly concerned about retaining a native-like accent. Finally, teaching one's native language necessitates sustained high use of the L1. Together, these factors suggest that the L1 accent of individuals who teach their native language may be especially protected from attrition.

On the other hand, teaching one's L1 in an L2-speaking environment requires regular use of the L2, not only in social contexts but also professionally. Thus, even if foreign language teachers aim to maximize the use of the target language in the classroom, regular recourse to the ambient language, and the use of both languages in alternation, is virtually inevitable (Littlewood and Yu 2011; Turnbull and Dailey-O'Cain 2009). Moreover, recent pedagogical approaches, notably "translanguaging" (Cenoz and Gorter 2019), have moved away from strict adherence to monolingualism and actively embrace the use of more than one language in language classrooms, in line with Cook's (2008) notion of "multicompetence" (see also (Illman and Pietilä 2018)). Individuals teaching their native language, therefore, need to keep both their L1 and L2 fully activated for extended periods, and hence operate in a sustained bilingual language mode in the classroom (Grosjean 2001). As discussed previously, this has been shown to enhance the likelihood of cross-linguistic interactions, and as a result changes to individuals' L1 accent (cf. (de Leeuw et al. 2010)).

In addition, language teachers are regularly exposed to L1-influenced pronunciations in their students' L2 productions. However, the effect that foreign-accented input has on their native speech patterns is unclear. On the one hand, experimental studies examining phonetic convergence in native–non-native dyads have failed to document instances of native speaker accommodation towards the accents of non-native speakers (Kim 2009; Kim et al. 2011), suggesting that language teachers may be impervious to the influence of their students' accented speech patterns. On the other hand, in these studies, accommodation is based on singular events during which rapid phonetic adjustments are assessed in conversations with unfamiliar individuals, and hence they do not allow conclusions to be drawn about the effects of repeated exposure to, and interaction with, familiar foreign-accented speakers in professional educational settings. It is certainly plausible that sustained accented input of this kind may affect the representations of teachers' L1 speech sounds, in line with Chang's (2019) Incidental Input Hypothesis, which argues that ambient input is incidentally processed and cannot be ignored. Moreover, evidence from both adults who were raised in bilingual homes (Bosch and Ramon-Casas 2011) and bilingual children in immersion school settings (Caldas 2006; Mayr and Montanari 2015) supports the idea that foreign-accented input may affect L1 pronunciation patterns. Thus, Bosch and Ramon-Casas (2011) showed that Catalan-Spanish bilinguals who were raised with both languages and received inconsistent phonetic input produced Catalan/e-ε/ less accurately as adults than bilinguals raised in Catalan-only homes. Caldas (2006), in turn, reported that his daughters' L1 French was English-accented, which he attributed to their exposure to non-native speech at their dual language school in Louisiana. In contrast, his son, who was solely educated through the medium of English, but like his sisters received native French input in the home, had a native-like accent in French. Similarly, Mayr and Montanari (2015) found that the two Italian-English-Spanish trilingual children in their study had native-like VOT patterns in Spanish, but English-accented ones in Italian, even though both languages contain a prevoiced—short lag VOT contrast. The authors attributed this finding to the fact that the children were regularly exposed to English-accented Italian from their classmates in their Italian-English dual language school in Los Angeles, while they only learnt Spanish from their monolingual Mexican nanny.

Based on these considerations, the present study sought to answer three inter-related research questions. First, it aimed to find out whether Spanish speakers who teach their native language in an L2-speaking environment are perceived as more or less native-like in their L1 than non-teachers in the same L2 environment who rarely use it. Second, it sought to determine to what extent perceptions of non-nativeness are characterized by individual variation. Finally, it attempted to identify the specific accentual features that are associated with non-native speech in native Spanish teachers and non-teachers who are long-term residents in an L2-speaking environment.

#### **2. Method**

An accent rating experiment was carried out in which monolingual Spanish listeners, resident in Spain, were exposed to short extracts of Spanish speech from a picture-based narrative produced by two groups of native Spanish speakers in the United Kingdom, language teachers, and non-teachers, alongside monolingual controls in Spain. Listeners were asked to state whether they detected a non-native accent in the speech samples and to provide an indication of their level of confidence in their judgement. Moreover, if they considered a sample to sound non-native, they were prompted to identify the accentual features that had led them to this conclusion.

#### *2.1. Participants*

Two groups of consecutive bilingual Spaniards living in the United Kingdom were recruited to participate in the study: (1) Spanish language teachers (BIL-T, *N* = 10, 9 females), and (2) non-teachers (BIL-NT, *N* = 9, 5 females). Those in the latter group practise a diverse range of professions, ranging from social work to accountancy and nursing, and none habitually use Spanish in their communication at work or at home. The participants in BIL-T, in turn, were either employed as Spanish teachers in schools (*N* = 5) or in university settings (*N* = 5). Further to being long-term residents—that is, having lived continuously in the UK for at least five years—an inclusion criterion for both of these groups was that migration took place after the age of 18. In this way, any differences identified in their speech can be attributed to attrition as opposed to incomplete L1 acquisition (Schmid 2014).

In addition to the two bilingual groups, a group of monolingual Spaniards residing in Spain participated in the study (MON, *N* = 8, 7 females). The speakers in this group had never lived anywhere other than Spain, had never spoken a language other than Spanish at home, as a medium of education or at work, and reported low levels of proficiency in English or any other language. As such, they meet Best and Tyler's (2007) definition of functional monolinguals as "not actively learning or using an L2" (p. 16).

Participants were recruited through ELE-UK (www.eleuk.org) and the Instituto Cervantes (www.ce rvantes.es), both of which are institutions dedicated to the teaching of the Spanish language, and through Spanish departments at English universities as well as via existing networks in the United Kingdom and in Spain. They came from a range of regions in Spain with no systematic differences across the groups: Andalusia (BIL-T: 2, BIL-NT: 1, MON: 1), Asturias (BIL-NT: 1, MON: 1), Castile-La Mancha (BIL-NT: 1, MON: 1), Catalonia (BIL-T: 2, BIL-NT: 1, MON: 1), Galicia (BIL-T: 3), Murcia (BIL-T: 1), Madrid (BIL-T: 1, BIL-NT:1, MON: 1), Basque Country (BIL-T: 1), Valencia (BIL-NT: 4, MON: 4).

All subjects gave their informed consent for inclusion before they participated in the study. The research reported in this manuscript was reviewed and approved by the Cardiff School of Health SciencesResearch Ethics Committee, Cardiff Metropolitan University, United Kingdom (ethics reference number: UG-265).

Initial contact was established by email and, in order to ensure groups were matched for key variables, demographic and linguistic background information was collected by means of an online questionnaire created using Qualtrics XM (Qualtrics 2019). A summary of participant characteristics is included in Table 1. Comparisons on all variables in the table were made across the two bilingual groups, while comparisons across all three groups were only made on the first three variables in the table, that is, education, English proficiency, and chronological age, as well as on gender distributions.

**Table 1.** Participant characteristics.

Notes: AOA = age of arrival in the UK; LOR = length of residence.

#### 2.1.1. Comparisons across the Two Bilingual Groups

The two bilingual groups were carefully matched on a range of background variables1. Thus, they did not differ from each other in gender distribution (Chi-square test: χ2(1) = 2.898, *p* = 0.089), chronological age (BIL-T (mean: 41.60, SE: 3.11); BIL-NT (mean: 33.56, SE: 2.16); Independent *t*-test: *t*(17) = 2.08, *p* = 0.053), age of arrival in the UK (BIL-T (mean: 28.20, SE: 2.78); BIL-NT (mean: 24.44, SE: 0.93); Independent *t*-test: *t*(17) = 1.223, *p* = 0.238) or length of residence (BIL-T (mean: 13.10, SE: 2.18); BIL-NT (mean: 8.89, SE: 1.84); Independent *t*-test: *t*(17) = 1.458, *p* = 0.163). Moreover, they were matched in terms of their highest level of education (BIL-T (median: 6.00, min-max: 5.00–7.00); BIL-NT (median: 5.00, min-max: 4.00–7.00); Mann–Whitney test: *U* = 27.500, *p* = 0.126), using a seven-point Likert scale ranging from 1 (less than secondary school education) to 7 (doctorate), as well as their self-reported competence in English (BIL-T (median: 4.00, min-max: 3.00–5.00); BIL-NT (median: 3.00, min-max: 3.00–5.00); Mann–Whitney test: *U* = 35.000, *p* = 0.374), based on a six-point Likert-type scale ranging from 1 (less than basic knowledge of English) to 6 (Native or near-native proficiency) in line with the classifications of the Common European Framework of Reference for Languages (Council of Europe 2001).

The bilingual groups were also matched on some of their language use patterns. Thus, they did not differ in their estimated use of Spanish and English in social situations outside their home and work in the UK (Spanish: BIL-T (mean: 33.00, SE: 5.16); BIL-NT (mean: 23.78, SE: 6.96); Independent *t*-test: *t*(17) = 1.079, *p* = 0.296; English: BIL-T (mean: 63.30, SE: 5.72); BIL-NT (mean: 76.22, SE: 6.96); Independent *t*-test: *t*(17) = 1.446, *p* = 0.166), the amount of time they spent in Spain per year (BIL-T (median: 1.00 (<1 month), min-max: 1.00 (<1 month) to 2.00 (1–3 months)), BIL-NT (median: 1.00 (<1 month), min-max: 1.00 (<1 month) to 2.00 (1–3 months)); Mann–Whitney test: *U* = 43.500, *p* = 0.879), the frequency of spoken contact with family and friends in Spain, for example, via telephone conversations (BIL-T (median: 1.00 (once or twice a week), min-max: 1.00 (once or twice a week) to 3.00 (less than once a month)); BIL-NT (median: 1.00 (once or twice a week), min-max: 1.00 (one or twice a week) to 3.00 (less than once a month)); Mann–Whitney test: *U* = 38.500, *p* = 0.492), or the frequency of written contact with family and friends in Spain, for example, email correspondence

1 To compare groups on scalar variables, such as chronological age, we ran parametric tests (independent *t*-test; one-way ANOVAs); for comparisons on ordinal variables and Likert scales, we ran non-parametric tests (Mann–Whitney test; Kruskal–Wallis test); the relation between nominal variables, in turn, was explored using chi-squared tests. When running independent samples *t*-tests across the two bilingual groups on the use of English and Spanish at work as well as on the use of Spanish at home, the variances turned out not be equal based on Levene's tests. In these cases, the *t*-values, *p*-values, and degrees of freedom were adjusted accordingly.

(BIL-T (median: 2.00 (once or twice a day), min-max: 1.00 (multiple times a day) to 4.00 (once or twice a month)); BIL-NT (median: 2.00 (once or twice a day), min-max: 1.00 (multiple times a day) to 3.00 (once or twice a week)); Mann–Whitney test: *U* = 27.500, *p* = 0.129).

In contrast, crucially, the two groups differed from each other in terms of their language use patterns in work, and to a lesser extent at home. Thus, the BIL-T group used English significantly *less* at work (BIL-T (mean: 54.20, SE: 7.48), BIL-NT (mean: 95.11, SE: 2.77); Independent *t*-test: *t*(11.390) = 5.130, *p* < 0.0005) and at home (BIL-T (mean: 41.50, SE: 12.26), BIL-NT (mean: 79.67, SE: 10.96); Independent *t*-test: *t*(17) = 2.30, *p* = 0.034) than the BIL-NT group, but Spanish significantly more at work (BIL-T (mean: 40.60, SE: 6.66), BIL-NT (mean: 4.33, SE: 2.69); Independent *t*-test: *t*(11.862) = 5.047, *p* < 0.0005) than the BIL-NT group. On the other hand, the two groups did not differ significantly from each other in their use of Spanish at home (BIL-T (mean: 33.50, SE: 12.03), BIL-NT (mean: 9.22, SE: 4.72); Independent *t*-test: (*t*(11.674) = 1.878, *p* = 0.086). Note that two of the BIL-T speakers and one of the BIL-NT speakers live by themselves and therefore indicated no use of any language in the home. Note also that the BIL-T speakers, but not the BIL-NT speakers, indicated occasionally using a language other than Spanish or English that was not specified further. This accounted for circa 5% of the use patterns at work and 7% at home.

#### 2.1.2. Comparisons across the Monolingual Group and the Two Bilinguals Groups

Finally, comparisons were made across all three groups, that is, BIL-T, BIL-NT, and MON. They differed significantly on self-rated competence in English (MON (median: 1.00, min-max: 1.00–2.00), BIL-T (median: 4.00, min-max: 3.00–5.00); BIL-NT (median: 3.00, min-max: 3.00–5.00); Kruskal–Wallis test: χ2(2) = 10.16, *p* = *0*.006) with a Dunn's post-hoc test revealing significantly lower scores for the MON group than BIL-T (*p* = 0.002) and BIL-NT (*p* = 0.016). Moreover, while the MON speakers did not differ from the other two groups in terms of gender distribution (Chi-squared test: χ2(2) = 3.873, *p* = 0.144), they differed in chronological age (MON (mean: 31.63, SE: 1.29), BIL-T (mean: 41.60, SE: 3.11); BIL-NT (mean: 33.56, SE: 2.16); One-way ANOVA: *F*(2,24) = 4.810, *p* = 0.018) and formal education level (MON (median: 4.50, min-max: 2.00–6.00), BIL-T (median: 6.00, min-max: 5.00–7.00); BIL-NT (median: 5.00, min-max: 4.00–7.00); Kruskal–Wallis test: χ2(2) = 6.74, *p* = 0.034), with the MON group significantly younger (*p* = 0.030) and less well educated (*p* = 0.029) than the BIL-T group, but not the BIL-NT group (*p* > 0.05).

#### *2.2. Speech Materials*

Participants audio-recorded themselves telling the story "I will help you" (Abbott et al. 2015) in Spanish. To do this, they were given access to an adapted version of the picture book online, which contained 17 pictures, but with all words removed. Participants could view the pictures as many times as they wished to ensure they understood the story before recording. Recordings were completed with a mobile phone or computer in a quiet environment, avoiding background noise, to promote optimum quality for subsequent use in the accent rating experiment. They were asked not to plan the exact wording beforehand and to imagine telling the story to a monolingual Spanish child. This approach was chosen to obtain quasi-spontaneous speech, whilst ensuring comparable samples in terms of lexical and grammatical content, and thus minimizing the likelihood of judgements resulting from differences in linguistic complexity (Schmid and Hopp 2014).

From each of the 27 narratives, a randomly selected speech sample of approximately 15 s was extracted in PRAAT (Boersma and Weenink 2019). This duration was considered sufficient for listeners to make a reliable judgement (de Leeuw et al. 2010; Flege 1984; Schmid and Hopp 2014). In order to minimize the likelihood that the listeners' judgements are based on areas other than pronunciation, samples were carefully screened to ensure they contained no lexical or grammatical errors and constituted grammatically complete utterances. Long pauses and hesitations were also avoided. A one-way ANOVA revealed no statistically significant difference in sample duration across groups (Mean BIL-T: 16.33 (SD: 1.14); Mean BIL-NT: 15.53 (SD: 0.763); Mean MON: 15.89 (SD: 0.524);

*F*(2,24) = 1.979, *p* = 0.160), nor in terms of speaking rate, as measured in syllables per second (Mean BIL-T: 5.21 (SD: 0.649); Mean BIL-NT: 5.54 (SD: 0.852); Mean MON: 5.70 (SD: 0.502); *F*(2,24) = 1.201, *p* = 0.318). To further reduce variability across samples, peak intensity was normalized, using PRAAT software (Boersma and Weenink 2019).

#### *2.3. Listeners*

The samples were presented as part of an online questionnaire created in Qualtrics XM software (Qualtrics 2019), which was distributed via an anonymous link to students at the Faculty of Education, University of A Coruña as well as existing networks across Spain. A total of 28 native Spanish listeners (20 females) with a mean age of 32 (SD: 11.25) completed the online accent rating experiment. Competence in English was controlled for with none of the listeners reporting higher than intermediate proficiency (mean 2.5, SD 0.75) comparable to the MON speakers' scores (cf. Table 1). Like the MON speakers, the listeners had never lived outside Spain and had never spoken a language other than Spanish at home, as a medium of education, or at work. Like the speakers, they come from a variety of regions, including Andalusia (*N* = 6), Castile-La Mancha (*N* = 1), Catalonia (*N* = 3), Extremadura (*N* = 1), Galicia (*N* = 3), Madrid (*N* = 1) and Valencia (*N* = 13).

#### *2.4. Experimental Procedure*

As the experiment was conducted online, listeners were given detailed written instructions regarding the task at hand. They were asked to use headphones, and an audio test was incorporated into the questionnaire to ensure adequate browser and volume settings had been selected. Participants were informed they would hear samples from fluent Spanish speakers, though no indication of whether they were native or not was given. Following the method established by Moyer (1999) and adopted in various studies on bilingual populations since then (e.g., (Bergmann et al. 2016; de Leeuw et al. 2010; Lloyd-Smith et al. 2020)), samples were played in random order and after each recording listeners were instructed to give a binary rating of the speaker's accent (native/non-native), indicating subsequently their degree of confidence (confident/neither confident nor not confident/not confident). They were further instructed to select "non-native" in the event they detected a non-native accent, however slight. Listeners heard each sample only once and were asked to guess if unsure, indicating their lack of confidence accordingly.

For samples rated "non-native", a follow-up question was included immediately after the rating was given, requesting details of what aspects of pronunciation had created a perception of non-nativelikeness, as well as any specific words that sounded non-native. In addition to the rating task, the questionnaire contained a range of demographic and language background questions to ensure the listeners met the inclusion criteria.

No time limit was imposed for responding and listeners controlled the pace at which they progressed through the samples. They were encouraged to take as many breaks as they deemed necessary. The average duration for the experiment was 25 min.

#### *2.5. Analysis*

In line with previous accent rating experiments (Bergmann et al. 2016; de Leeuw et al. 2010; Moyer 1999), listeners' responses were converted to a six-point scale in which a "native" rating marked as "confident" appeared at one end of the scale (1) and a "confident" rating as "non-native" at the other (6). As such, the lower the numerical foreign accent rating (FAR), the nearer to nativelike the speaker was perceived to be. The experimental data were subsequently transferred to a CSV file for statistical analysis. In order to assess whether the groups differ in their FAR, linear mixed-effects models were run in R (R Core Team 2018) using the LmerTest function (Kuznetsova et al. 2017). To analyze the features identified by the listeners, content analysis was used (Krippendorff 2018). This first involved screening responses for relevant phonetic information. Comments that did not relate to accentual features were disregarded. Items referring to accentual features, in turn, were initially coded as relating

to either segmental or suprasegmental phenomena before being assigned to more specific subcategories. These were then quantified. As a measure of reliability, coding was repeated on all 174 comments, yielding an agreement score of 95.98%. Divergences between the two sets of analysis only concerned a small number of comments with unclear/ambiguous meanings. For example, reference to "una pronunciación muy marcada, muy fuerte" (a very marked pronunciation, very strong) was coded as referring to rhythm/stress in the first analysis, but as being too general to include in the re-analysis. These comments were discarded from further analysis.

#### **3. Results**

#### *3.1. Accent Rating*

To assess inter-rater reliability, we ran a Cronbach's alpha analysis across the ratings made by the 28 listeners. The results revealed a value of 0.81, which suggests a high degree of homogeneity. Figure 1 depicts the distribution of FAR scores across the three groups.

Inspection of the figure suggests that the samples were predominantly perceived to be native-like, with median scores of "1" for the participants in BIL-NT and MON, and of "2" for the participants in BIL-T, although the scores in all groups exhibited a certain degree of variation. Overall, a total of 221 of the 28 × 27 = 756 samples were rated as non-native, that is, 29.23%, with 107 (i.e., 14.15%) attracting the highest FAR score of "6", that is, "non-native with certainty". To examine whether the FAR scores differed across the groups, linear mixed-effects models were run in R (R Core Team 2018), with "group" as fixed effect and "participant" as random intercept. Using the LmerTest function (Kuznetsova et al. 2017), the Satterthwaite approximation was used to obtain degrees of freedom, from which *p*-values could be calculated.

**Figure 1.** Distribution of FAR scores by group.

Our initial model was run on all 756 ratings and across the three groups. The results, depicted in Table 2, revealed highly significant between-group differences (*p* < 0.001). This analysis was subsequently followed up with pairwise comparisons, with a Bonferroni-adjusted α-level of 0.0167.

**Table 2.** Results of linear mixed-effects models: FARs.

The results revealed significantly higher and thus less native-like FAR scores for the participants in BIL-T than in BIL-NT (*p* < 0.001) and MON (*p* < 0.001). The difference between the latter two, in contrast, was not significant (*p* = 0.053). Together, these results suggest that the L1 accent of Spaniards in non-teaching professions in the UK was perceived as equally native-like as that of monolinguals resident in Spain. Spaniards teaching their L1 in educational settings in the UK, in contrast, whilst also attracting relatively low FAR scores, were perceived as significantly less native-like, suggesting a certain degree of L1 attrition.

#### *3.2. Perceived Non-Native Features*

All 28 listeners provided comments on the samples they deemed non-native; however, this was only the case for 174 of the 221 samples (i.e., 78.73%), while 47 of the non-native ratings were left uncommented. Following a careful screening, 71 of the 174 comments were removed from the analysis as they were too general, referring, for example, just to "pronunciation of some words" or "the speaker's accent", and an additional three were removed that referred to features unrelated to pronunciation, for example, lexical or grammatical choice. The remaining 100 comments were analysed further; of these, 84 referred to a single feature, while 13 referred to 2 features, and 2 to 3 features, for a total of 116 feature tokens. Table 3 shows a breakdown of the features identified, alongside illustrative examples. Since they did not exhibit any systematic differences between the speakers in BIL-T and BIL-NT, the data were pooled.

**Table 3.** Perceived non-native features.

**Table 3.** *Cont*.

Inspection of the table shows that judgements of non-nativeness were based on both segmental and suprasegmental features, albeit with a preponderance of the former. Amongst segments, listeners most commonly perceived consonantal items as non-native, notably realizations of /s/ and rhotic consonants, but some also referred to vowel deviations and phoneme omissions. Comments on suprasegmental items predominantly referred to intonation, mostly expressed in terms of "melodía" (melody) or "musicalidad" (musicality), but there were also some mentions of rhythm/stress and speaking rate.

#### *3.3. Individual Variation*

Finally, in addition to the analysis at the group level, we investigated individual variation. This was done by converting median FARs into a categorical rating of "clearly native" (between 1.0 and 2.5), "uncertain" (greater than 2.5 but less than 4.5), and "clearly non-native" (between 4.5 and 6.0) following de Leeuw et al.'s (2010) approach. The categorizations for the participants in the three groups are shown in Table 4.

**Table 4.** Categorization of nativeness by group.

Inspection of the table shows that, as one would expect, all MON speakers were consistently classed as "clearly native". In contrast, in line with previous work on attrition (e.g., (de Leeuw et al. 2010, 2018a; Mennen 2004)), the results for the two bilingual groups were more varied. Thus, although the BIL-NT speakers were not found to differ from the MON ones at the group level, as we have seen, the analysis of individual classifications shows that one BIL-NT speaker was considered "uncertain" and another one "clearly non-native". At the same time, while 4 in the BIL-T group were classed as "clearly non-native" and one as "uncertain", half of them were considered "clearly native". As a result, teaching one's native language in an L2-speaking environment does not automatically lead to perceived attrition in L1 speech; it merely appears to increase its likelihood. Table 5 displays the characteristics of the participants identified as non-native.

**Table 5.** Characteristics of participants perceived as non-native.

Note: AN = Andalusia; CT = Catalonia; GA = Galicia; PV = Basque Country; the figures in parenthesis denote the number of comments per feature.

As the table shows, all participants considered "clearly non-native" were female, aged between 27 and 48 years, and considered their English competence as upper intermediate to near-native. They had moved to the United Kingdom in their twenties or thirties and had been living there between 5 and 18 years. Of the 28 listeners, 20 or more considered the four BIL-T speakers as non-native; slightly fewer listeners, that is, 17, classified BIL-NT\_1 as non-native. The latter also received a slightly lower FAR and was hence perceived as less clearly non-native than the four BIL-T speakers. Finally, the table

shows that the Spanish accent of each of these participants was associated with multiple non-native features. All were perceived to produce their L1 with non-native intonation patterns and realizations of /s/, and all but one, that is, BIL-T\_2, were perceived to realize Spanish rhotics non-natively.

#### **4. Discussion**

This study aimed to gain a better understanding of the role of L1 use and dual language activation in the perceived attrition of native speech patterns. To this end, we examined the L1 Spanish accent of two groups of native Spanish speakers who are long-term residents in the UK, Spanish language teachers and non-teachers, alongside monolingual Spanish speakers in Spain in an accent perception experiment. The results revealed significantly greater non-native ratings for the teachers than the non-teachers and the monolinguals, but no difference between the latter two, with listeners' impressions of non-nativeness based on a range of segmental and suprasegmental features. An analysis of individual patterns, in turn, showed a fair amount of variation, with half of the speakers in BIL-T perceived as "clearly native" and one of the BIL-NT speakers as "clearly non-native". In what follows, the implications of these findings will be discussed.

To begin with, let us consider why the participants in BIL-T were perceived as significantly more foreign-accented in their L1 than monolinguals in Spain. At first glance, this finding is surprising. After all, they regularly use their L1 both in work and outside of it, and Spanish plays an essential role in their professional identity. As Chang (2019, p. 108) states, being a language teacher typically comes with an "instructional orientation", and likely coincides with a particular concern for retaining native-like proficiency in the L1, including its accent, although this was not formally assessed here. One might expect these factors to provide a certain degree of protection from attrition. However, this was not the case in the present study, at least not at the group level.

The likely reason for the perceived attrition in the teachers' L1 accent is dual language activation, which, in turn, is a direct consequence of the specific professional setting in which they operate. In other words, it is essentially impossible for foreign language teachers who teach their L1 in an L2-speaking environment to activate only their L1 during classroom activities and only their L2 outside of it, and hence function in alternate monolingual language modes (Grosjean 2001). Instead, both their languages need to be highly active for most or all of the time, resulting in them operating in a sustainable bilingual language mode. This will be true even if the extent of dual activation varies somewhat from context to context. For example, it is likely to be particularly high during activities that actively encourage a bilingual approach, such as translanguaging (Cenoz and Gorter 2019), while it will be comparatively lower during activities in which sole use of the target language is encouraged, in particular in students with high L2 proficiency levels. Nevertheless, whatever the specific circumstances, the very nature of foreign language classroom settings makes dual activation inevitable.

Crucially, dual activation has been shown to lead to cross-linguistic interactions in speech patterns. Such interactions have been widely attested in contexts of phonetic code-switching (Amengual 2018; Bullock and Toribio 2009; Muldner et al. 2019; Piccinini and Arvaniti 2015), where cognitive demands to inhibit the non-target language are particularly high. While they may initially occur in such circumstances, that is, during ad hoc dual language activation, over time they may give rise to more persistent accentual changes and become entrenched. This is likely to have happened to the teachers in the present study and is consistent with de Leeuw et al.'s (2010) finding that L1 attrition was more common in native German speakers in Anglophone Canada and the Netherlands who regularly used their L1 in contexts of code-switching than those who did not.

In addition, unlike the non-teachers, the teachers will have been systematically exposed to non-native Spanish accents via their students' productions. These may have either independently caused the observed changes in their L1 accent or enhanced the effects of their own concurrent use of the two languages, thereby reinforcing deviations from monolingual Spanish patterns. While the direct effect of sustained English-accented input in Spanish cannot be isolated in the present context, it will have led to an additional burden on teachers' inhibitory control mechanisms. The suggestion that

foreign-accented input can increase the likelihood of non-native speech patterns is certainly consistent with evidence from adults raised in bilingual homes (Bosch and Ramon-Casas 2011) as well as bilingual and multilingual children in immersion school settings (Caldas 2006; Mayr and Montanari 2015), although its role in L1 attrition of speech needs to be explored further in future research. Taken together, the results for the participants in BIL-T suggest that, ironically, it is the very nature of the professional context in which teachers operate, with its requirement to keep both languages active and the need to switch between them, that enhances the likelihood of L1 attrition.

The participants in BIL-NT, in contrast, do not face these cognitive demands in a professional setting. While they work in a diverse range of areas, such as nursing, social work, and accountancy, none of them involve professional use of Spanish. As a result, the BIL-NT speakers virtually exclusively use their L2 in work, and hence operate in a consistent monolingual English language mode. Their lower overall amount of L1 use (and greater amount of L2 use), compared with the BIL-T group, in turn, did not lead to perceived attrition since they were rated the same as monolingual controls in Spain. Previous research suggests a somewhat ambiguous role for overall amount of language use in L1 attrition: while some studies have shown an effect of reduced L1 contact on attrition of speech patterns (e.g., (Stoehr et al. 2017)), others either revealed no effect (e.g., (Hopp and Schmid 2013)), or exhibited mixed results. For example, Chang (2019) showed no greater overall persistence in L1 phonetic drift in English-Korean bilinguals with high L2 use compared to those with low L2 use—only one of three areas investigated yielded a significant effect. While it is conceivable that the complete lack of L1 use over many years may cause attrition, independent of other factors, due to the gradual loss of long-term memory representations, this was not the case here. After all, even though the participants in BIL-NT hardly ever used their L1 in work contexts, they indicated using it regularly in social interactions outside of work as well as in written and spoken forms of remote communication with family and friends in Spain. The reduction in L1 use that typically occurs in L2 immersion contexts is hence unlikely to cause L1 attrition of speech in and of itself. It appears that what is critical is the contexts in which the L1 is used (cf. (Schmid 2007)). In the present study, it may well be the absenc*e* of L1 use in the kinds of contexts in which the teachers use their native language professionally, that has protected the BIL-NT speakers' speech from attriting. At the same time, their high L2 competence will have protected them from experiencing L1 phonetic drift as a result of a novelty effect (Chang 2012, 2013).

These considerations notwithstanding, the results of the present study also show a fair amount of individual variation, with half of the participants in BIL-T being perceived as "clearly native-like" and one participant in BIL-NT as "clearly non-native". Moreover, while the BIL-T participants were rated as significantly more non-native than those in BIL-NT and MON, their median FAR was "2", that is, "native-like with medium confidence". This suggests that L1 attrition in the context of teaching one's L1 in an L2-speaking environment is by no means inevitable. Perhaps the five teachers in BIL-T who were rated as "clearly native" in Spanish developed enhanced inhibitory control which allowed them to counteract cross-linguistic interactions from dual language activation and exposure to foreign-accented speech by their students. This may have coincided with a range of factors relating to individual differences, such as attitudinal, socio-psychological, and cognitive ones. For example, they may ascribe particular importance to the retention of a native accent in Spanish. Or they may have a particular phonetic talent (e.g., (Jilka 2009; Lewandowski and Jilka 2019)). Moreover, they may actually be perceived as non-native, but only in settings not assessed here, for example, in casual encounters (Major 1992). By the same token, the absence of particular skills or attitudes may explain attrition in BIL-NT\_1's L1 accent. However, explanations of this nature remain wholly speculative as these variables were not investigated in the present study. Suffice it to say that L1 attrition of speech is a complex multi-factorial phenomenon (cf. (Kartushina et al. 2016b)) and that the patterns observed here must have been caused, in part, by factors other than dual language activation and language use. Although challenging, future work, based on a larger sample of potential attriters, is needed that systematically teases the various predictor variables for L1 attrition of speech apart and includes a more sophisticated approach to the assessment of L1 use. In the context of language teachers, this could

involve obtaining details on interaction patterns with students of varying levels of proficiency during different types of classroom activity, but also language use and code-switching patterns with fellow foreign language teachers outside the classroom.

While we have so far discussed differences between the teachers' and non-teachers' use of languages at work, their language use patterns at home also need to be considered. Our results showed that BIL-T not only differed from BIL-NT participants in their language patterns in the workplace, but also in their language patterns at home. Crucially though, the language differences at home only pertained to the use of the L2, which was used more frequently by the non-teachers than the teachers. In contrast, no differences were found between the two groups in their use of Spanish at home. This shows that the perceived attrition in the teachers' L1 accent, cannot be explained by a reduction in L1 use at home, given that their amount of L1 use was similar to that of the non-teachers.

Finally, let us consider the features that the listeners associated with non-native speech. They encompass a range of consonants, vowels, and prosodic phenomena, in particular realizations of /s/, rhotics and intonation patterns, in line with evidence that perceptions of non-nativeness arise from the interplay between segmental and suprasegmental characteristics (Ulbrich and Mennen 2016). Importantly, there were no systematic differences in the features associated with non-nativeness in the BIL-T and BIL-NT speakers. Moreover, the speech of all speakers who were identified as "clearly non-native" was characterized by multiple non-native features and at both segmental and suprasegmental levels. This suggests that listeners did not erroneously mistake them as non-native due to their unfamiliarity with individual features that are associated with native dialectal variation, such as the phenomenon of seseo/ceceo in the context of /s/ (Martínez-Celdrán et al. 2003). While the features identified must have been perceptually salient for the listeners, their relative importance to the impression of non-nativeness remains unclear. Moreover, the listeners' judgements may have been influenced by accentual patterns that they were not consciously aware of or that they were unable to verbalize. It is also difficult to ascribe the features to specific types of interaction with L2 English, for example, assimilation or dissimilation patterns (cf. SLM (Flege 1995; Flege and Bohn 2020)), due to a lack of detail in the comments provided. Future research exploring the salience of features in global accent ratings is needed that extends the work presented here, using a more sophisticated methodology, such as an interactive interview-based approach (Mayr et al. 2020) or one that allows listeners' judgements to be linked directly to specific items in the speech samples (Montgomery and Moore 2018).

#### **5. Conclusions**

The present study examined the role of L1 use and dual language activation in L1 attrition by investigating the perceived L1 accent of two groups of native Spanish speakers in the United Kingdom: (1) Spanish language teachers, who use their L1 regularly in professional settings that require frequent switching between Spanish and English, and (2) non-teachers, who virtually never use their L1 in the workplace. In addition, the study included a control group of monolingual speakers in Spain. As such, this study is the first to examine L1 attrition of speech systematically in a specific professional group. The results of a global accent rating experiment revealed significantly greater non-native ratings for the teachers than the non-teachers and the monolingual controls, but no difference between the latter two. Listeners' impressions of non-nativeness, in turn, were based on a range of segmental and suprasegmental features, notably /s/, rhotics and intonation. These results suggest that language teachers who teach their L1 in an L2-speaking environment may be particularly prone to L1 attrition. This is likely due to a need to co-activate both their languages in professional settings as well as regular exposure to non-native speech from L2 learners. In contrast, low L1 use was not associated with non-native features in the non-teachers' Spanish accents. Together, the findings hence suggest that cross-linguistic interaction is more likely to lead to L1 attrition of speech than reduced L1 use in and of itself. However, since not all teachers were perceived as non-native, future research based on a larger sample is needed that assesses the factors further that facilitate or hinder L1 attrition in such educational settings.

**Author Contributions:** Conceptualization, R.M., D.S. and I.M.; methodology, R.M. and D.S.; formal analysis, R.M. and D.S.; writing—original draft preparation, R.M. and D.S.; writing—review and editing, R.M., D.S. and I.M.; visualization, D.S. and R.M.; supervision, R.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

Bylund, Emanuel, Kenneth Hyltenstam, and Niclas Abrahamsson. 2013. Age of acquisition effects or effects of bilingualism in second language ultimate attainment'. In *Sensitive Periods, Language Aptitude, and Ultimate L2 Attainment*. Edited by Gisela Grañena and Mike Long. Amsterdam: John Benjamins, pp. 69–101.

Caldas, Stephen J. 2006. *Raising Bilingual Biliterate Children in Monolingual Cultures*. Clevedon: Multilingual Matters.

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## *Article* **The E**ff**ect of Instructed Second Language Learning on the Acoustic Properties of First Language Speech**

**Olga Dmitrieva 1,\*, Allard Jongman 2 and Joan A. Sereno 2**

Received: 1 September 2020; Accepted: 20 October 2020; Published: 26 October 2020