# Learning to Detect Bipolar Disorder and Borderline Personality Disorder with
Language and Speech in Non-Clinical Interviews

###### Abstract

Bipolar disorder (BD) and borderline personality disorder (BPD) are both
chronic psychiatric disorders. However, their overlapping symptoms and common
comorbidity make it challenging for the clinicians to distinguish the two
conditions on the basis of a clinical interview. In this work, we first
present a new multi-modal dataset containing interviews involving individuals
with BD or BPD being interviewed about a non-clinical topic . We investigate
the automatic detection of the two conditions, and demonstrate a good linear
classifier that can be learnt using a down-selected set of features from the
different aspects of the interviews and a novel approach of summarising these
features. Finally, we find that different sets of features characterise BD and
BPD, thus providing insights into the difference between the automatic
screening of the two conditions.

Index Terms: bipolar disorder, borderline personality disorder, spoken
dialogue, computational paralinguistics, path signature

##  1 Introduction

Bipolar disorder (BD) is a recurrent chronic mental health condition which
occurs in approximately 1% of the global population [1]. It is characterised
by episodes of low and high mood which cause significant interference with
everyday life. Borderline personality disorder (BPD) is characterised by a
long-term pattern of constantly variable mood, self-image and behaviour.
Although BD and BPD are two very different conditions they share some similar
symptoms such as mood instability and impulsive behaviour [2]. A recent study
[3] reported the high prevalence of comorbidity between the two conditions,
with up to 21.6% of individuals with BD found to have comorbid BPD. As a
result they can be difficult to distinguish, but accurate diagnosis is crucial
as they require different treatment [4, 5]. Standard diagnostic assessment
involves a psychiatrist asking a series of questions about symptoms and the
person has to retrospectively describe their account of these symptoms. The
success of the assessment also relies on how the psychiatrist interprets both
the verbal and non-verbal cues drawn from the person’s responses. In this
work, we aim to develop a method that extracts cues automatically from
interviews conducted in a non-clinical setting, to assist the existing
assessment framework, which is expensive and subjective.

Recent studies have explored data driven approaches to automatically screen
patients, incorporating features extracted from multiple modalities in
clinical interviews, showing diagnostic value for mental health conditions
such as depression and bipolar disorder [6, 7, 8, 9]. [8] finds the
performance of automatic mood detection to be much better in clinical
interactions than in personal conversations, and there are significant
differences in the features important to each type of interaction. While
existing studies of BD have focused on recognising mood episodes, the
distinction between BD and BPD remains understudied. In this paper, we aim to
bridge this gap by presenting a multi-modal (i.e. speech and text) dataset
containing interviews in a non-clinical setting involving individuals with a
diagnosis of BD or BPD, and study the automatic assessment of the two mental
health conditions.

Motivated to study the interaction between the interviewer and participant
during the course of an interview from different aspects (including linguistic
complexity, semantic content and dialogue structure), we investigate features
extracted from different modalities. Path signatures, initially introduced in
rough path theory as a branch of stochastic analysis, has been shown to be
successful in a range of machine learning tasks involving modelling temporal
dynamics [10, 11, 12]. We propose to apply path signatures for summarising
features extracted from each utterance, sentence and speaker-turn into
interview-level feature representations, given its ability to naturally
capture the order of events. By doing so, we automatically include more non-
linear prior knowledge in our final feature set, which leads to effective
classification, even with a simple linear classifier.

The contributions of this work are as follows: (1) We present a new non-
clinical interview dataset involving BD and BPD patients; (2), We investigate
different feature types and propose using path signatures as a novel approach
of summarising turn-level features; (3) We demonstrate a good linear model can
be learnt for three classification tasks, and provide insights into the
distinction between BD and BPD by analysing the importance of the selected
features.

##  2 AMoSS Interview Dataset

The original Automated Monitoring of Symptoms Severity (AMoSS) study [13, 10]
was a longitudinal study during which a range of wearables in combination with
a bespoke smartphone app were used for the daily self-monitoring of mood
instability. Among the 139 participants enrolled in the study, 53 had a BD
diagnosis, 33 had been diagnosed with BPD and 53 were healthy volunteers. All
the diagnoses had been confirmed prior to the study using the structured
clinical interview for DSM-IV (the 4th edition of Diagnostic and Statistical
Manual of Mental Disorders) and the International Personality Disorder
Examination (IPDE) [14]. The majority of the BD participants were euthymic
while BPD participants were not in crisis but were still symptomatic as is the
case with chronic experience of the condition. Exclusion criteria for BD and
BPD were comorbidity of each diagnosis [15].

62 participants were interviewed halfway through the study to gather
qualitative feedback and discuss potential improvement111The study protocol
was approved by the NRES Committee East of England—Norfolk (13/EE/0288), and
all 62 participants consented to be interviewed and for those interviews to be
recorded.. Each participant was interviewed only once. These semi-structured,
one-on-one qualitative interviews took place either in person or by telephone,
conducted by 2 clinicians and 2 psychology graduates who were involved in the
roll out of the AMoSS study. The natural conversations recorded between the
interviewer and participant are usually within the scope of: 1) experience
using the mood reporting app and the questionnaires in the app; 2) experience
using different wearable devices; 3) benefits of taking part in the study and
discussion of potential improvement, making the interviews semi-structured.

The AMoSS interview (AMoSS-I) dataset we study here consists of 50 randomly
sampled interviews that were initially transcribed by the same interviewers.
The audio recordings and manually transcribed text were then aligned by a
Sakoe-Chiba Band Dynamic Time Warping based forced speech alignment model in
aeneas222https://github.com/readbeyond/aeneas. This was followed by a
convolutional neural network (CNN) based noise-robust speech segmentation
[16], generating speaker-turn-level alignments in the time domain. The manual
transcripts and automatically generated time alignments were then reviewed and
improved by three research assistants, where each interview was reviewed twice
to ensure quality and consistency. We used
finetuneas333https://github.com/ozdefir/finetuneas as the annotation interface
for reviewing. The demographic details of the participants are summarised in
Table 1. We can see both BIS-11444The Barratt Impulsiveness Scale (BIS-11)
[17] is a self-report questionnaire designed to assess the
personality/behavioral construct of impulsiveness. Higher BIS-11 scores are
indicative of higher impulsivity. and IPDE scores are higher among BD and BPD
patients compared to controls. We also observe from the density plots in
Figure 1 that distributions of the three user groups are very similar in
interview length and number of participant responses.

| BD | BPD | HC
---|---|---|---
#Interviews | 21 | 17 | 12
#Room Interviews | 14 | 9 | 9
#Phone Interviews | 7 | 8 | 3
Gender (m:f) | 7:14 | 1:16 | 3:9
Age (years) | $44\pm{17}$ | $34\pm{21}$ | $34\pm{18.5}$
BIS-11 score | $67\pm{16}$ | $76\pm{22}$ | $48.5\pm{10.25}$
IPDE score | $2\pm{5}$ | $16\pm{3}$ | $0\pm{0}$
Table 1: Demographic characteristics of the three groups: Bipolar disorder
(BD), Borderline personality disorder (BPD) and Healthy controls (HC).
Appropriate distributions are summarised in the form of the median +/- the
interquartile range.

Figure 1: Density plots of: length of the interview (top), and number of
responses from the participants (bottom) Figure 2: Data pipeline including
three stages of data preprocessing (in purple), feature extraction (in green)
and classification (in orange).

###  2.1 Data Preprocessing

Some of the interview recordings show noticeable difference of loudness
between two speakers, mainly due to the mode of recording. In order to
alleviate the effect of loudness difference, we scale the audio signal for
each speaker turn separately, and make sure each turn is in the range of -1
and 1. We also perform intra-speaker-turn segmentation using an end-to-end
voice activity detection (VAD) model [18] trained on the DIHARD Speech
Diarisation data [19]. The model extracts domain-independent features during
its domain-adversarial multi-task training on DIHARD, showing better
performance over the standard VAD models that do not use such domain
information. This in turn allows us to extract dialogue features related to
pauses in speech appearing within each speaker turn.

##  3 Feature Extraction

We identify a set of features that are motivated by existing work in automatic
mental health assessment and clinical studies of bipolar disorder symptoms.
These features are selected to allow studying the interviews from different
aspects : lexical diversity and density, syntax, semantic content and dialogue
structure.

Linguistic complexity features (LING): Previous studies have shown language
disturbances such as pressure of speech in mania and poverty of speech in
depression are among the main symptoms of acute episodes in BD [20, 21]. We
adopt a set of linguistic complexity measures used in [9], including
measurements for lexical diversity such as moving average type-to-token ratio
(MATTR), brunét’s index (BI) and honoré’s statistic (HS); lexical density such
as number of function words per word (FUNC/W) and number of interjections per
word (UH/W); and mean length sentence (MLS). These features are shown to be
effective in distinguishing patients with schizophrenia and bipolar I
disorder. We also use the dependency-based propositional idea density (DEPID),
originally proposed in [22] for measuring the rate of propositions or ideas
expressed per word in spontaneous speech transcripts.

In addition to the aforementioned measures we extract part-of-speech related
variables including: first person pronouns (it has been shown that people
suffering from depression use more first person pronouns in written [23] and
spoken language [24]); swear words; speech disfluencies; filler words/phrases
such as “okay” and “you know”, using the LIWC-2015 dictionary [25]. Finally,
we add the number of absolutist words per word (ABS/W) as a feature using a
19-word absolutist dictionary curated by clinical psychologists [26], who
found that the online anxiety, depression, and suicidal ideation forums
contained more absolutist words than the control ones. Overall we extract 28
linguistic features, denoted as LING.

Semantic content features (CNT): We extract content related to psychological
states from the transcripts by applying relevant categories of LIWC [27] such
as emotions (e.g. anxiety), social processes (e.g. family) and drives (e.g.
reward and risk). We also apply the empathetic concern and personal distress
lexica [28], and an optimism lexicon (i.e. positive future-oriented thinking),
by taking the sum over all weighted words multiplying their relative
frequencies in the speaker turn. Overall we use 19 content features.

Dialogue features (DIAL): We use the set of high-level turn-taking behaviour
related features proposed in [7], namely relative floor control, turn hold
offset (i.e. short pauses that are less than half a second), number of
consecutive turns (separated by longer than half a second pauses), turn switch
offset and turn length, per speaker turn. Turn switch offset measures the
latency between speaker turns, and it is shown that depressed people take more
time to respond to questions by clinicians [29]. While Relative floor control
measures the percentage of time an individual controlled the conversation
floor up to the time of speaking, we also add relative turn length that
measures the percentage of the length of the current turn relative to the
average turn length up to the time of speaking. We compute both features in
seconds as well as in number of words, following [30].

Talking over or interrupting others, is one of the characteristics for
pressure speech, which has shown to be a significant feature in bipolar mania
[31, 32]. We use a LSTM-based overlapping speech detection model proposed in
[33] to extract the number and duration of speech overlaps in each speaker
turn as features. Additionally, we add the number of words per second per
turn, as a feature representing speaking speed.

We average turn hold offset and speech overlaps per turn, in addition to
number of consecutive turns, relative floor control (time), relative floor
control (words), relative turn length (time), relative turn length (words),
turn switch offset, turn length and number of words per second, which results
in 11 features representing each speaker turn.

###  3.1 Interview-level Feature Representation

The theory of rough paths, developed by Lyons [34], can be thought of as a
non-linear extension of the classical theory of controlled differential
equations. The signature of a path555We refer the reader to [35] for a
rigorous introduction of path signatures, and [36] for a primer on its use in
machine learning. (i.e. an ordered data stream) is a collection of $n$-fold
iterated integrals such that every continuous function of the path may be
approximated arbitrarily well by a linear function of its signature. Motivated
by its ability to naturally capture the order of events and model temporal
dynamics, we apply signature transform (SIG), which is the map from a path to
its signature, to each type of the turn-level features and generate interview-
level fixed-length feature representation.

##  4 Experiments and Analysis

Following previous work we chose leave-one-participant-out (LOOCV) as the
evaluation scheme, and logistic regression as the classification model given
our preference over interpretability and the size of our data. For each fold,
we first apply signature transform to each type of turn-level features, and
keep only the first three levels of the path signature666We use iisignature
Python library, https://pypi.org/project/iisignature/, and set the maximal
order to which iterated integration is performed in signature to be 3.. To
avoid overfitting, we conduct feature selection on signature-transformed
interview-level features through computing Pearson Correlation Coefficients
(PCC) with the IPDE scores on the training data and retain the features with
$p$-values less than 0.001. This results in a small number of features. The
selected features are then fed to the classifier for 3 separate binary tasks:
(1), BD vs. healthy controls, (2), BPD vs. healthy controls, and (3), BD vs.
BPD patients. We conduct three separate experiments, extracting features from
the speech of each participant and interviewer respectively, as well as the
whole interview (as a sequence of turns) without speaker identification
(denoted as ‘Both’)777For the ‘Interviewer’ and ‘Both’ experiments, we
increase the p value threshold to 0.002..

###  4.1 Analysis of the selected features

Five highly ranked and most commonly selected features from each task are
briefly summarised in Table 8 as examples. Each interview-level feature is
represented as a linear combination of the original turn-level features. We
see over half of the selected features are volume integrals, i.e. they are
triple integrals of three turn-level features, while the rest are double
integrals which give the Lev́y area. We notice most all of the representative
features are from the linguistic category (many are part-of-speech tags),
especially for H vs. BD and H vs. BPD, showing the significance of the
structure in the interviews. This is inline with the finding in [8], and
extends to nonclinical interviews conducted partially by students.

We also notice the appearances of nonfluencies (Nonflu.) especially in
combination with conjunctions (CONJ) in the detection of H vs. BPD. The use of
the absolute words (ABS) in combination with common adverbs (ADV) and
negations (NEG) or article words (e.g. a, an, the) are selected for the two
classification tasks involving BPD. Two interview-level features (DEPID,
MATTR, BI) and (BI, MATTR, MLS) are shown to be the most commonly selected for
the two tasks involving BD.

H vs BD | H vs BPD | BD vs BPD
---|---|---
(DEPID, MATTR, BI) | (Nonflu., CONJ) | (BI, MATTR, MLS)
(Nonflu., Verbs) | (ABS, ADV, Articles) | (We, PREP)
(PPRO, CONJ, CONJ) | (WPS, SP_avg, RFC_t) | (PREP, We)
(NEG, AUXV, NEG) | (CONJ, Nonflu.) | (ABS, ADV, NEG)
(PPRO, Swear, Verbs) | (You, Verbs, Nonflu.) | (SOC, DRI, DRI)

Table 2: Top-5 highly ranked and most commonly selected features during LOOCV.
Feature selection is based on correlation between each feature and the IPDE
scores of the training samples. Features belong to the linguistic category are
colored in blue; dialogue feautres are in green and content features are in
red.888PPRO: personal pronouns; PREP: prepositions; ADV: adverbs; AUXV:
auxiliary verbs; CONJ: conjunctions; NEG: negations; Nonflu.: nonfluencies;
We: first-person plural; Swear: swear words; MLS: mean length sentence; WPS:
number of words per second; SP_avg: average length of short pauses; RFC_t:
relative floor control (time); SOC: social processes; DRI: drives.

###  4.2 Results and Discussion

The results for the classification tasks are summarised in Table 3. Using the
(late) fusion of linguistic, dialogue and content features with signature
transform, we obtain a AUROC of 0.810 in H/BD, 0.733 in H/BPD and 0.817 in
BD/BPD. We notice the result in H/BPD is significantly worse than the other
two tasks. We leave its investigation to future work, we think the fewer data
samples and varying recording quality may have contributed to the worse
performance. When we model from the speaking segments of the interviewers, we
obtain very poor performance. As the purpose of the interviews were merely to
understand the individual’s experience of taking part in the AMoSS study
rather than establishing their mental state at the time of interview, it is no
surprise that features extracted from the interviewers have very weak
discriminative power. We also believe having different interviewers impacted
negatively on the classifications. Modelling the interviews as a sequence of
utterances also resulted in much worse performance than learning from the
participants alone.

| AUROC
---|---
Subject | H/BD | H/BPD | BD/BPD
Participant | 0.810 | 0.733 | 0.817
Interviewer | 0.304 | 0.473 | 0.231
Both | 0.494 | 0.431 | 0.657

Table 3: Classification results for three binary tasks: H vs. BD, H vs. BPD
and BD vs. BDP, using logistic regression. Results shown are average AUROCs
across all interviews.

We also conduct ablation experiments to examine how performance changes after
removing each feature type. As seen in Table 4, the linguistic features (LING)
are the biggest contribution in all three tasks. As a consequence, we have to
increase the p-value feature selection threshold from 0.001 to 0.005 to have
any feature for classification, if we remove LING. A sharp performance drop is
then observed removing LING in in all three tasks. If we remove both LING and
dialogue features (DIAL), the results get even worse. If we exclude both DIAL
and content features (CNT), we still get reasonably good performance without
significant drop in AUROC. It’s also worth noticing the ineffectiveness of the
content features (CNT) apart from in BD vs. BPD, possibly due to the semi-
structured nature of the interviews and the questions asked fall within the
same scope. Given that the majority of BD and BPD participants were clinically
stable this may also account for the relatively poor distinction between the
groups using CNT features that are related to the psychological states from
responses by the participants.

Features | H vs BD | H vs BPD | BD vs BPD
---|---|---|---
All | 0.810** | 0.733** | 0.817**
All-CNT | 0.810** | 0.733** | 0.787**
All-DIAL | 0.768** | 0.733** | 0.811**
All-LING | 0.625* | 0.578* | 0.669*
All-LING-CNT | 0.642* | 0.703* | 0.604*
All-LING-DIAL | 0.442* | 0.429* | 0.550*
All-CNT-DIAL | 0.768** | 0.733** | 0.763**

Table 4: Feature ablation results (AUROC) for each task, e.g. the final row in
the table shows the result for using linguistic features (LING) only. p-value
used for feature selection: ‘**’$<$0.001; ‘*’$<$0.005.

##  5 Conclusions

In this paper, we demonstrate the potential of using features extracted from
language and speech in non-clinical interviews to assist the assessment of
bipolar disorder BD and borderline personality disorder BPD, which is
challenging for clinicians to distinguish. We first presented a non-clinical
interview dataset, named AMoSS-I, conducted partially by psychology graduates,
for the task of detecting BD and BPD. We demonstrated good performance in
three classification tasks using down-selected features and a new way of
summarising these features based on path signatures. Lastly, we showed the
importance of linguistic features in all three tasks and the benefits of
feature fusion from different modalities. For future work, we plan to learn
acoustic features, and investigate the effect of acoustic properties of the
interviews and the impact of recording environments.

##  6 Acknowledgements

This work was supported by the MRC Mental Health Data Pathfinder award to the
University of Oxford [MC_PC_17215], by the NIHR Oxford Health Biomedical
Research Centre and by the The Alan Turing Institute under the EPSRC grant
EP/N510129/1. We would also like to thank Zakaria Aldeneh and Kairit Sirts for
sharing code and thoughts, and to Priyanka Panchal and Rota Silva for
conducting the interviews. The views expressed are those of the authors and
not necessarily those of the NHS, NIHR or the Department of Health.

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