# Acoustic Characterization of Huntington's Disease Emotional Expression: An Explainable AI Approach

Lucie Chenain, Anne-catherine Bachoud-Lévi, Chloé Clavel

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Lucie Chenain, Anne-catherine Bachoud-Lévi, Chloé Clavel. Acoustic Characterization of Huntington's Disease Emotional Expression: An Explainable AI Approach. ACIIW 2024 - 12th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, Sep 2024, Glasgow, United Kingdom. hal-04934178

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# Acoustic Characterization of Huntington's Disease Emotional Expression: An Explainable AI Approach

Lucie Chenain

NPI/ALMANACH

ENS/INSERM/UPEC/INRIA/Paris Cité

Paris, France

lucie.chenain@ens.psl.eu

Anne-Catherine Bachoud-Lévi

NPI

APHP/ENS/INSERM/UPEC

Paris, France

anne-catherine.bachoud-levi@aphp.fr

Chloé Clavel

ALMANACH

INRIA

Paris, France

chloe.clavel@inria.fr

**Abstract**—Huntington's Disease (HD) is a neurodegenerative disorder characterized by motor, cognitive, and psychiatric symptoms. The first studies on the emotional behavior of HD patients, based on speech emotion recognition (SER) models, have recently emerged. Following on from this line of research, we propose in this article to study two as yet unexplored points: 1. The ability of HD individuals to express emotions using emotional vocal narration; 2. The nuances of acoustic expression across various emotions in HD patients. In the first experiment, emotional stories narrated by HD, premanifest HD (preHD), and control individuals were rated to analyze patients' ability to follow the given instructions. HD subjects showed difficulties in telling happy and angry stories compared to control subjects, while preHD only showed difficulties in telling angry stories. The second experiment is dedicated to analyzing the acoustic expression of emotions in the patients. For this purpose, emotion classifiers were developed and a Shapley Additive exPlanations (SHAP) analysis was performed to gain insights into the different acoustic expressions of emotions of the groups. The classifiers performed satisfactorily, except for the emotion anger in HD and joy in preHD. This indicates that the acoustic expression of these emotions is more difficult to perceive, even if the subjects were able to follow the given instructions. The SHAP analysis showed that the features that are important for the prediction of emotions differ from one group to another. This study contributes to a deeper understanding of emotional speech narration in HD and preHD individuals, highlighting not only the deviations from controls, but also uncovering specific acoustic patterns indicative of emotional expression in the HD group.

**Index Terms**—Huntington's Disease, speech processing, emotional expression, emotion recognition model, pathological speech

## I. INTRODUCTION

Current healthcare systems may not adequately address the needs of neurodegenerative disease patients. Limited access to medical experts, geographical disparities in care availability, and long delays in obtaining medical appointments create significant challenges for managing these progressive conditions.

This work was funded by the European Union's Seventh Framework Programme (FP7) for research, technological development and demonstration under grant agreement number 602245, the Henri-Mondor Hospital National Reference Centre for Huntington's disease (Ministry of Health), grants from the Agence Nationale pour la Recherche (ANR-17-EURE-0017 Frontcog, ANR-10-IDEX-0001-02 PSL*), NeurATRIS (ANR-11-INBS-0011) and partially funded by the ANR-23-CE23-0033-01 SINNet project. LC is a student from the FIRE PhD program funded by the Bettencourt Schueller foundation and the EURIP graduate program (ANR-17-EURE-0012).

Among the different neurodegenerative disorders, Huntington's Disease (HD) is one of the most challenging diseases to tackle due to the heavily impacting disease consequences. HD is an incurable autosomal dominant disorder caused by a repeated expansion of the cytosine-adenine-guanine (CAG) trinucleotide (≥ 39) in the *Huntingtin* gene. The disease is characterized by motor, cognitive, and psychiatric impairments [1]. Its progression includes an asymptomatic premanifest phase (preHD) followed by manifest phase [2]. As there are currently no disease-modifying treatments, symptom monitoring and management are critical for patients' well-being [3].

Remote monitoring and teleconsultation offer a promising approach to overcome the challenges faced by HD patients in their healthcare. Indeed, digital tools like conversational agents can be a solution to maintain contact with patients between medical appointments. Collecting data on their emotional well-being can help clinicians address patients' needs and provide a personalized healthcare approach [4], [5]. The integration of speech emotion recognition (SER) models in conversational agents offers an opportunity to build a more comprehensive picture of patients' emotional well-being. Such models can help to analyze the emotional expression of patients with neurodegenerative diseases in comparison to control subjects [6], [7], diagnose psychiatric behaviors such as depression [8]–[10] and help with psychiatric differential diagnosis [11].

The understanding HD individual's affective behavior has only recently been investigated using SER models. The expression of emotions in their speech was less recognized in both preHD individuals and controls when evaluated by a machine learning model and a human panel [12]. In this study, the SER model used a Random Forest classifier trained with both the acoustic speech signal and a textual representation of patients' recordings. The emotional corpus consisted of recordings of participants who were asked to tell emotional stories corresponding to the emotions happy, sad, angry, and neutral. It was found that people with HD are impaired in this emotional expression compared to preHD and control subjects. Statistical differences were found between HD and controls for restrained audio features grouped by respiratory, phonatory, and articulatory features. In other words, this shows that emotional speech deficits are characterized across the entire speech production. However, considering that HD patients have dysprosody [13], defined as a deficit in the
production of appropriate speech melody and rhythm, their acoustic expression of emotions remains unknown and uncharacterized. With advances in explainable artificial intelligence (AI) methods, such as Shapley Additive exPlanations (SHAP), we can now identify the key speech features that a model uses to discriminate between different emotions. Integrating SHAP into research addresses the challenge of interpretability of artificial intelligence models. It eliminates the black box effect by providing direct insights into the model's decision-making process. Furthermore, by distinguishing the importance of global and local features, researchers can gain direct insight into the key contributing features [14].

We propose to investigate within the same corpus the emotional expression of HD and to obtain a precise characterization of the acoustic expression of emotions in carriers of the HD gene. We aim to lay the groundwork for future research on this topic, which has remained largely unexplored until now. To achieve an accurate characterization, we propose to take another perspective and first investigate the patients' ability to follow the given instructions, being aware that they have language disorders [15] and may perseverate from one task to the next. Therefore, in the first experiment, the ability of HD gene-carriers to follow instructions when asked to tell an emotional story was assessed by comparing them with healthy controls. For this purpose, each speech recording was rated using emotional Likert scales. In the second experiment, only recordings in which the instructions were followed and which contained sufficient emotional content were retained. An emotion classifier was then created for each group to distinguish different emotions based on individuals' acoustic expressions. We then investigate the potential of explainable AI approaches such as SHAP to characterize the acoustic expression of emotions in HD individuals.

## II. DATASET

### A. Participants

138 participants' clinical information and recordings were collected from two observational cohorts (REPAIR-HD NCT03119246 and BIO-HD NCT01412125) at the Hôpital Henri Mondor (Créteil, France) (Table I). All participants were French native speakers, aged ≥ 18 years, and signed an informed consent form. Exclusion criteria were incapacity to consent and significant neurological or psychiatric comorbidities unrelated to HD. Ethics approval was given by the institutional review board from Henri Mondor Hospital (Créteil, France) for the BioHD cohort and the CPP Saint Louis French part of the Repair-HD cohort, in compliance with the Helsinki Declaration.

The database included 101 HD gene-carries (CAG ≥ 38), among which 73 manifest HD individuals at an early disease stage (stage I or II [16]) and 28 preHD individuals. PreHD participants were defined with a Total Motor Score (TMS) ≤ 5 and a Total Functional Capacity (TFC) = 13 [17], [18]. Additionally, 37 healthy controls (HC) without any cognitive deficits were included (Mattis Dementia Rating Scale ≥ 136 [19]). All participants were recorded under the same conditions

<table><caption>TABLE I
DEMOGRAPHICS AND CLINICAL INFORMATION</caption><thead><tr><th></th><th>HD</th><th>preHD</th><th>HC</th></tr></thead><tbody><tr><td>N=</td><td>73</td><td>28</td><td>37</td></tr><tr><td>Age</td><td>53.94 ± 10.63</td><td>48.56 ± 10.9</td><td>53.04 ± 7.73</td></tr><tr><td>Sex</td><td>40F, 33M</td><td>14F, 14M</td><td>18F, 19M</td></tr><tr><td>Education</td><td>13.07 ± 3.07</td><td>14.43 ± 2.13</td><td>13.46 ± 3.08</td></tr><tr><td>CAG</td><td>43.62 ± 2.97</td><td>41.64 ± 2.11</td><td></td></tr><tr><td>TMS</td><td>32.54 ± 14.33</td><td>0.54 ± 1.14</td><td>0.49 ± 0.99</td></tr><tr><td>TFC</td><td>10.46 ± 2.12</td><td>13.0 ± 0.0</td><td>13.0 ± 0.0</td></tr></tbody></table>

in a hospital consultation room following their neuropsychological assessment. They sat at a table facing the interviewer, with the recording device placed on the table 20 cm from the participant. The device used was a ZOOM H4n Pro recorder, with a sampling rate of 44.1 kHz and a 16-bit resolution. No acoustic engineer was needed to install this setup.

### B. Speech task instructions

The quality of an emotional speech database is crucial for the development of a SER model. Incomplete, inaccurate, and erroneous data can lead to incorrect classification. Emotional speech corpus can be divided into three groups. Acted emotion databases contain emotional speech performed by professional or semi-professional actors, but they may exaggerate emotions beyond real-life expressions. Elicited databases evoke emotions in speakers by simulating emotional situations, resulting in expressions that are closer to real-life emotions. Natural speech databases consist of spontaneous speech extracted from broadcasts, providing authentic expressions. However, their acquisition is challenging due to ethical and legal considerations [20]. In this study, the emotional speech corpus used is an elicited one, as individuals were prompted to narrate emotional stories. This has the advantage of collecting spontaneous speech, making the speech corpus authentic to everyday communication.

Individuals were recorded by certified neuropsychologists. The spontaneous speech tasks aimed to elicit emotional and non-emotional stories from the speakers in French. The stories were prompted by standardized questions (example: "Can you tell me a pleasant story, full of happiness and joy, something joyful. Why do you think it is associated with happiness?"). Participants were instructed to spontaneously narrate a neutral story (describing their last 24 hours), followed by 3 emotional stories (sad, angry, and happy stories), in this fixed order. The emotional speech tasks were separated by non-emotional speech tasks to avoid emotional bleed-over between them. The recording sessions finished on the recall of a happy story to avoid ending with a negative emotion. Participants were blinded to the study's goals, and could stop the session anytime if they felt uncomfortable or fatigued.

### C. Data preprocessing

The recordings were divided into speech tasks by speech therapists and then transcribed using the Praat and Seshat softwares. The speech therapists then divided the recordings into
stretches of continuous speech, delimited by voice inflections
and breathing pauses to exclude unintelligible words [21], [22].

### III. EXPERIMENT 1: CONGRUENCE BETWEEN EXPECTED EMOTIONS AND IDENTIFIED EMOTIONS

#### A. Methods

1) **Emotional annotation:** To assess whether the speakers followed the instructions, an emotional rating of the speech task recordings was carried out. The recordings were pre-processed by removing the audio segments containing explanations or speech from the neuropsychologist. This ensured that the annotators were unaware of the specific instructions while listening to the recordings. Five annotators received a thorough briefing, during which the annotation protocol was taught and practiced in a training phase. Each annotator randomly received a set of speech task recordings, comprising one-fifth of the corpus, along with the corresponding linguistic transcripts. For each speech task, the annotator listened and rated 4 items on a Likert scale (0: not at all; 1: slightly, 2: moderately; 3: a lot, and 4: completely), corresponding to the degree of emotional content associated with the four possible instructed emotions:

* Item 1: How happy do you think this story is?<fnref n="1" />

* Item 2: How sad do you think this story is?<fnref n="2" />

* Item 3: How angry do you think this story is?<fnref n="3" />

* Item 4: How neutral do you think this story is?<fnref n="4" />

To ensure that the participants followed the instructions, the annotators were instructed to prioritize the linguistic emotional content over the acoustic cues. This allowed us to check whether the expected emotion prompted by the instruction received a high or low rating on its corresponding item. For example, if a participant is instructed to narrate a happy story, their recording should receive a high rating for item 1 (How happy does this story seem to you?). This method also controlled for situations where participants naturally switched to a different type of emotion than intended. For example, a participant who was instructed to tell a sad story might begin their narrative and then deviate and begin a separate narrative focusing on anger. This ensured the creation of a reliable emotional speech corpus.

2) **Statistical analysis of emotional ratings:** The Likert ratings were assessed for inter-rater reliability using the Kendall coefficient of concordance. This was performed on 10 stories drawn randomly and independently of the groups, for each of the 4 Likert rating scales, across the 5 different annotators. This agreement measure is adapted for inter-rater evaluation involving more than two raters and ordinal data. The annotators reached good agreement with an average of 0.89 (happy=0.774, sad=0.912, angry=0.890, and neutral=0.978) [23].

The Kruskal-Wallis non-parametric test was used to evaluate significant differences in how speech tasks were rated across groups for their corresponding item. Multiple comparison correction was applied to the p-values using the Benjamini-Hochberg procedure, controlling for false discovery rate. The effect size for the Kruskal-Wallis test was computed with the eta-squared ($\eta^2$), based on the H-statistic ($H$). Pairwise group comparison was performed with Dunn's post-hoc test.

3) **Criteria for instruction-following recordings:** Following the evaluation of the emotional annotation ratings, inclusion and exclusion conditions were established to define which recordings were considered to follow or not follow the instructions:

* **Inclusion:**

- Condition 1: For a recording to be considered as following the instructions, the recording needed to receive a rating $\ge 3$ for its expected item. For example, if the instructed emotion was happy, the recording should receive a rating of $\ge 3$ only for Item 1 ("How happy do you think this story is?")

* **Exclusion:**

- Condition 2: If the recording did not have any item with a rating $\ge 3$.

- Condition 3: If the recording has only one item rated $\ge 3$ but the item is not the expected one. For example, the instructed emotion happy gets a single rating $\ge 3$ for Item 2 ("How sad does this story seem to you?").

- Condition 4: If the recording had more than two items rated $\ge 3$, irrespective of whether the corresponding item was the highest rated or not.

- Condition 5: If more than two items had equal ratings, regardless of whether the corresponding item was one of them or not.

#### B. Results

1) **Instruction control statistical analysis:** For each narrated story, the rating of its corresponding item was statistically compared across the three groups (Fig. 1). For happy instructions, the Kruskal-Wallis test indicated group differences ($H = 18.29, p \le 0.001, adjusted p \le 0.001$), specifically between HD and HC ($p \le 0.001$) but not between HD and preHD ($p = 0.078$), as indicated by the pairwise comparison Dunn's test. preHD differed from HC ($p = 0.050$). Additionally, a large effect size was observed ($\eta = 0.15$). For the angry story instructions, group differences were identified ($H = 7.61, p = 0.02, adjusted p = 0.04$), between HD and HC ($p = 0.01$), but not between HD and preHD ($p = 0.08$). preHD performed similarly to controls ($p = 0.55$). The effect size was moderate ($\eta = 0.06$). This reveals a difference of behavior between HD gene-carriers, compared to healthy participants. HD's lower scores on the happy and angry Likert scales suggest they had difficulty recalling and narrating stories that evoked these emotions. In contrast, preHD individuals showed only a lack of ability to follow instructions for

<footnote n="1">¹"A quel point cette histoire vous semble joyeuse?"</footnote>

<footnote n="2">²"A quel point cette histoire vous semble triste?"</footnote>

<footnote n="3">³"A quel point cette histoire vous semble colérique?"</footnote>

<footnote n="4">⁴"A quel point cette histoire vous semble neutre?"</footnote>
narrating happy stories. This inability to follow instructions could be related to the psychiatric profiles of patients. Indeed, apathy, a motivational disorder characterized by a lack of goal-directed behavior and diminished emotional responses, is very common in HD individuals and can affect their emotional expression [24]. The remaining analysis of sad and neutral stories ratings showed similar behavior across groups after multiple test corrections (respectively *H* = 1.70, *p* = 0.43, adjusted *p* = 0.43, *η* = 0.01 and *H* = 4.99, *p* = 0.08, adjusted *p* = 0.11, *η* = 0.04).

2) Instruction-following recordings: After applying the inclusion-exclusion criteria in section III.3, it was observed that 73.09% of all recordings had adhered to the given instructions (Table II). Recordings of individuals who did not follow the instructions accounted for 19.57% and 5.92% of the dataset for Conditions 2 and 3 respectively, with HD individuals making up to 65.69% and 70.97% of those recordings, respectively. As for the remaining recordings that did not adhere to the instructions, their presence was minimal, with Conditions 4 and 5 collectively comprising less than 2% of the dataset.

<table><caption>TABLE II
EMOTION LABELING ACCORDING TO THE INSTRUCTION CONTROL PROTOCOL RATINGS</caption><thead><tr><th></th><th>Condition 1: only the expected item ≥ 3</th><th>Condition 2: No item rated ≥ 3</th><th>Condition 3: one Only unexpected item ≥ 3</th></tr></thead><tbody><tr><td>Recordings %</td><td>73.09</td><td>19.57</td><td>5.92</td></tr><tr><td>Instructions %</td><td></td><td></td><td></td></tr><tr><td>Neutral</td><td>30.55</td><td>9.80</td><td>3.23</td></tr><tr><td>Happy</td><td>25.07</td><td>22.55</td><td>19.35</td></tr><tr><td>Sad</td><td>25.54</td><td>20.59</td><td>38.71</td></tr><tr><td>Anger</td><td>19.86</td><td>35.29</td><td>38.71</td></tr><tr><td>Groups %</td><td></td><td></td><td></td></tr><tr><td>HD</td><td>48.30</td><td>65.69</td><td>70.97</td></tr><tr><td>preHD</td><td>21.93</td><td>17.65</td><td>16.13</td></tr><tr><td>HC</td><td>29.77</td><td>16.67</td><td>12.90</td></tr></tbody></table>

All instruction and group percentages represent the ratio among the recordings for each condition

## IV. EXPERIMENT 2: ACOUSTIC ANALYSIS

By conducting a second experiment focused on categorizing emotions in individuals carrying the HD gene, valuable insights have been gathered into their emotional expression. This lays the foundation for developing digital tools aimed at assessing the mental health and well-being of HD patients.

### A. Methods

1) Speech features extraction: For each stretch of the recordings, acoustic speech features were extracted using the extended Geneva Minimalistic Acoustic Parameter Set variant v02 (eGeMAPSv02) [25]. This set of features was created for common standards for emotional features in research, to ensure comparability of results and eliminate potential parameter variability. This provides a common framework for emotion recognition systems. Functionals are extracted for all features, except for the temporal ones, with the mean, and standard

<figure><img src="image_2.png" /><figcaption>Fig. 1. Items rating of the instructed emotions</figcaption></figure>
deviation normalized by the arithmetic mean. For loudness and pitch, the 20th, 50th, and 80th percentile are added, along with the range of 20th to 80th percentile. A total of 88 features were thus extracted. The overlapping windows of F0, harmonic differences, HNR, jitter, and shimmer are 60ms and 10ms apart. For loudness, spectral slope, spectral energy proportions, formants, harmonics, Hammarberg index, and Alpha ratio the overlapping windows are 20 ms and 10 ms apart. F0 is standardized by being converted from its linear Hz scale to a logarithmic scale. The extracted features have been classified into prosodic, spectral, and voice quality categories (Table III).

<table><caption>TABLE III
EXTRACTED SPEECH FEATURES</caption><thead><tr><th>Feature Type</th><th>Acoustic Features</th></tr></thead><tbody><tr><td>Prosodic</td><td>Pitch with logarithmic fundamental frequency (F0) on a semitone frequency scale<br>Loudness<br>Loudness peaks per second<br>Number of continuous voiced regions per second<br>Continuously voiced and unvoiced regions<br>Equivalent sound level</td></tr><tr><td>Spectral</td><td>Formant 1 (F1), 2 (F2) and 3 (F3) frequency<br>F1, F2, F3 relative energy<br>F1, F2, F3 bandwidth<br>Alpha ratio<br>Hammarberg index<br>Spectral slope 0-500 Hz and 500-1500 Hz<br>MFCC 1-4<br>Spectral flux<br>Harmonic difference H1-H2 and H1-A3</td></tr><tr><td>Voice Quality</td><td>Jitter<br>Shimmer<br>Harmonics-to-noise ratio (HNR)</td></tr></tbody></table>

2) Classification models: Classifiers were created for each group by utilizing the extracted acoustic features from the stretches, categorizing them as anger, happiness, sadness, or neutral. The priority was to build a model suitable for SHAP analysis, rather than achieving the absolute best performance.

For each classifier, the dataset was split into a training-validation set (80%) and a testing set (20%). As the datasets had an unbalanced number of stretches across emotions, the imlearn package Synthetic minority over-sampling (SMOTE) was applied only to the training-validation sets to avoid data leakage in the testing sets [26]. This approach generated a balanced subdataset without loss of information.

Hyperparameter tuning of a random forest classifier model was performed with a 5-fold inner cross-validation loop. This determined the best classification model for each group (parameters are available in the open-access repository). Stratified 10-fold cross-validation was used to train and validate the model to ensure generalizability. The data was divided into 10 datasets with 10 permutations, 9 of which were used for training and 1 for validation. The metrics used to evaluate the model were macro precision, macro recall, macro F1, and area under the curve (AUC).

Subsequently, the developed model and a dummy model returning the most frequent class were evaluated with the remaining 20% testing dataset. This enabled the assessment of classification performance on novel data.

<table><caption>TABLE IV
EMOTIONAL CORPUS CONTENT SUMMARY</caption><thead><tr><th>Group</th><th>HD</th><th>preHD</th><th>HC</th></tr></thead><tbody><tr><td><strong>Sad labels</strong></td><td></td><td></td><td></td></tr><tr><td>Rec. count</td><td>47</td><td>19</td><td>28</td></tr><tr><td>Rec. duration</td><td>48.66 ± 38.21<br>[8.36 ; 166.35]</td><td>58.79 ± 28.24<br>[18.88 ; 126.54]</td><td>57.22 ± 38.77<br>[20.1 ; 164.02]</td></tr><tr><td>Stretch count</td><td>759</td><td>388</td><td>486</td></tr><tr><td>Stretch duration</td><td>3.01 ± 3.2 [0.11<br>; 23.3]</td><td>2.93 ± 3.3 [0.16<br>; 21.81]</td><td>3.3 ± 3.37 [0.21<br>; 23.72]</td></tr><tr><td><strong>Happy labels</strong></td><td></td><td></td><td></td></tr><tr><td>Rec. count</td><td>44</td><td>21</td><td>31</td></tr><tr><td>Rec. duration</td><td>56.35 ± 42.32<br>[12.01 ; 178.62]</td><td>48.88 ± 28.07<br>[18.5 ; 132.17]</td><td>58.27 ± 37.84<br>[15.45 ; 145.6]</td></tr><tr><td>Stretch count</td><td>858</td><td>327</td><td>573</td></tr><tr><td>Stretch duration</td><td>2.89 ± 2.84<br>[0.14 ; 21.89]</td><td>3.14 ± 3.45<br>[0.17 ; 21.01]</td><td>3.15 ± 3.43<br>[0.18 ; 22.94]</td></tr><tr><td><strong>Angry labels</strong></td><td></td><td></td><td></td></tr><tr><td>Rec. count</td><td>37</td><td>18</td><td>21</td></tr><tr><td>Rec. duration</td><td>52.95 ± 44.09<br>[15.66 ; 236.67]</td><td>63.09 ± 31.67<br>[30.46 ; 148.32]</td><td>60.88 ± 37.85<br>[19.32 ; 207.03]</td></tr><tr><td>Stretch count</td><td>599</td><td>406</td><td>357</td></tr><tr><td>Stretch duration</td><td>3.27 ± 3.43<br>[0.16 ; 24.06]</td><td>2.8 ± 2.91 [0.15<br>; 22.47]</td><td>3.58 ± 3.9 [0.13<br>; 22.69]</td></tr><tr><td><strong>Neutral labels</strong></td><td></td><td></td><td></td></tr><tr><td>Rec. count</td><td>57</td><td>26</td><td>34</td></tr><tr><td>Rec. duration</td><td>54.19 ± 34.03<br>[16.65 ; 193.61]</td><td>81.56 ± 45.38<br>[23.22 ; 220.19]</td><td>61.45 ± 31.69<br>[16.13 ; 151.47]</td></tr><tr><td>Stretch count</td><td>1171</td><td>678</td><td>610</td></tr><tr><td>Stretch duration</td><td>2.64 ± 2.47<br>[0.14 ; 18.66]</td><td>3.13 ± 3.05<br>[0.15 ; 21.38]</td><td>3.42 ± 3.27<br>[0.14 ; 25.22]</td></tr><tr><td><strong>Total hours of<br>emotional speech<br>(hours)</strong></td><td>2.73</td><td>1.5</td><td>1.88</td></tr></tbody></table>

All displayed duration of recordings (Rec.) and stretches are in seconds, unless specified otherwise.

3) SHAP: To understand what characterized each group's emotional speech, SHAP was applied to interpret the models' classification [27]. Based on game theory, SHAP assigns a value that explains the contribution of each feature in the generation of the outcome. Since SHAP is model-independent, it is compatible with any machine-learning model. It was applied to the testing datasets. The features with absolute SHAP values ≥ 0.006 were automatically ranked from highest to lowest impact on the model output. This threshold was chosen because it effectively filtered out the features with minimal impact on the models, thereby increasing the models' interpretability. These features were assessed for group difference using ANOVA, followed by Tukey's correction method for multiple comparisons.

## B. Results

1) Labels of the emotional corpus: After applying the exclusion and inclusion criteria to select recordings with emotional content (see section III.1.3), the stretches of the recordings were labeled with the instructed emotion. The total duration of speech per group was 2.73 hours in HD, 1.5 hours in preHD, and 1.88 hours in HC. Table IV shows each group's remaining number of recordings per emotions, the number of stretches, and their average duration.

2) Model performance: The models were evaluated using testing sets (Table V). For each group, the developed model
outperformed the dummy model's performance and showed good classification performance. This indicates that the acoustic signal in each group seems to contain enough information to automatically distinguish the different emotions. However, the class-specific performance varied, indicating that the models had more difficulties to classify certain emotions. For example, the recall score for anger is low in the HD and HC models (0.24 and 0.23, respectively), suggesting instances of this emotions may be misclassified. The confusion matrix of the HD model (Fig. 2), reveals that a significant proportion of instances of anger is misclassified as sadness (44.88%) while in preHD, anger is misclassified as sadness and happiness in equal proportions (both 30.67%). One plausible explanation for this could be that the stretches were labeled according to the corresponding emotional story, so perhaps some stretches from the recording may not have as much acoustic cues than others.

Similarly, in the preHD model, the classification of happy had a low recall (0.15), indicating that the model missed identifying some instances of happy. However, its precision, measuring the accuracy of positive predictions, remained high. This suggests that when the model predicted happy, it was correct. This could be caused by the complexity of the expression of happiness which may vary from one individual to another, also endorsed by the fact that preHD had significantly lower emotional ratings on the happy Likert scale item in experiment 1 (section III.B.1).

<table><caption>TABLE V
MODELS' TESTING PERFORMANCE</caption><thead><tr><th></th><th>Metric</th><th>HD</th><th>preHD</th><th>HC</th></tr></thead><tbody><tr><td rowspan="4">Anger</td><td>Precision</td><td><strong>0.67</strong></td><td>0.53</td><td>0.46</td></tr><tr><td>Recall</td><td>0.24</td><td><strong>0.44</strong></td><td>0.23</td></tr><tr><td>F1</td><td>0.36</td><td><strong>0.48</strong></td><td>0.30</td></tr><tr><td>ROC</td><td><strong>0.77</strong></td><td><strong>0.77</strong></td><td>0.72</td></tr><tr><td rowspan="4">Happy</td><td>Precision</td><td>0.55</td><td><strong>0.67</strong></td><td>0.45</td></tr><tr><td>Recall</td><td><strong>0.50</strong></td><td>0.15</td><td>0.46</td></tr><tr><td>F1</td><td>0.52</td><td>0.25</td><td>0.46</td></tr><tr><td>ROC</td><td><strong>0.75</strong></td><td><strong>0.75</strong></td><td>0.66</td></tr><tr><td rowspan="4">Sad</td><td>Precision</td><td><strong>0.53</strong></td><td>0.39</td><td>0.43</td></tr><tr><td>Recall</td><td>0.38</td><td>0.27</td><td><strong>0.49</strong></td></tr><tr><td>F1</td><td>0.44</td><td>0.32</td><td><strong>0.46</strong></td></tr><tr><td>ROC</td><td><strong>0.75</strong></td><td>0.72</td><td>0.72</td></tr><tr><td rowspan="4">Neutral</td><td>Precision</td><td>0.48</td><td>0.47</td><td><strong>0.51</strong></td></tr><tr><td>Recall</td><td>0.77</td><td><strong>0.81</strong></td><td>0.61</td></tr><tr><td>F1</td><td>0.59</td><td><strong>0.60</strong></td><td>0.56</td></tr><tr><td>ROC</td><td>0.69</td><td><strong>0.77</strong></td><td>0.75</td></tr><tr><td rowspan="4">Model performance</td><td>Precision</td><td><strong>0.56</strong></td><td>0.52</td><td>0.46</td></tr><tr><td>Recall</td><td><strong>0.47</strong></td><td>0.42</td><td>0.45</td></tr><tr><td>F1</td><td><strong>0.48</strong></td><td>0.41</td><td>0.44</td></tr><tr><td>ROC</td><td>0.74</td><td><strong>0.75</strong></td><td>0.71</td></tr><tr><td rowspan="4">Dummy model performance</td><td>Precision</td><td>0.09</td><td>0.09</td><td>0.08</td></tr><tr><td>Recall</td><td>0.25</td><td>0.25</td><td>0.25</td></tr><tr><td>F1</td><td>0.13</td><td>0.13</td><td>0.12</td></tr><tr><td>ROC</td><td>0.5</td><td>0.5</td><td>0.5</td></tr></tbody></table>

3) SHAP interpretation: The SHAP values identified the important features allowing the model to classify emotions. Table VI displays the features that have the greatest impact on the model, showing only those with an absolute SHAP mean value of ≥ 0.006. Detailed statistical comparison of features between groups are reported in the supplementary material.

### C. Common features

The SHAP analysis results revealed common acoustic features that were important for emotion classification across all groups (unhighlighted features in Table VI), with 4 shared features for happy, 6 for sad, and 5 for neutral. For example, we see that five pitch features (F0 mean, standard deviation, percentiles 50 and 80, and 0-20 range) were consistently important for recognizing sadness in the three groups.

When looking at the specific types of features used for emotion classification, all three groups relied on prosodic features approximately half of the time. However, the HD model relied slightly less on those (48%), compared to preHD (55%) and HC (55%).

### D. Specific features

Each group was predominantly defined by distinct feature characteristics when expressing emotional speech. First, the HC group exhibited 25 unique features, which is more than preHD and HD which respectively had 20 and 18 unique acoustic features (see the highlighted features in Table VI).

Second, HD and preHD speech exhibited fewer features that allowed the models to discriminate emotions. Specifically, the HD group had 29 discriminating features with an absolute SHAP value ≥ 0.006, similar to the preHD group with 27, which is less than the 38 controls' discriminating features.

Third, spectral features were less informative for emotion recognition in the preHD (26%) and HC (42%) groups compared to HD (52%). This suggests that spectral information may be as informative as the prosodic one in the differentiation of emotions for the HD model. This enables us to understand the emotional dysprosody identified in this population, defined as a deficit in producing appropriate melody and rhythm of speech during emotional expression [13], [28]–[30].

Interestingly, the features used by the models to recognize anger were different for each group. The HD group relied only on two spectral features (alpha ratio and spectral flux), while in contrast, the preHD group used three prosodic features related to intensity (loudness) and pitch variation (F0 standard deviation). This compared to the HC group which relied mostly on prosodic pitch features (four F0 measures), with additionally one prosodic rhythm feature (voiced segment length) and two spectral ones (MFCC3 and F1 bandwidth).

Regarding the expression of neutral speech, our findings are in accordance with previous studies reporting increased pitch fluctuations in HD individuals [31]–[33], compared to HC. These non-emotional speech disturbances have been associated with inefficient nervous system control and chorea [34], [35].

The post-hoc analysis (see supplementary results) revealed subtle differences in how each group expressed acoustic features across the same emotions. For example, for the sadness emotional expression, the HC and preHD groups had higher pitch compared to the HD group.

## V. CONCLUSION

Our study yielded three conclusions. First, HD patients showed difficulties in telling both happy and angry stories
<figure><img src="image_3.png" /><figcaption>Fig. 2. Groups' validation set confusion matrices</figcaption></figure>

<table><caption>TABLE VI
ABSOLUTE SHAP VALUES (≥ 0.006) PER GROUP OVER THE DIFFERENT EMOTIONS</caption><thead><tr><th>Emotion</th><th>HD</th><th>preHD</th><th>HC</th></tr></thead><tbody><tr><td>Angry</td><td>1. Alpha Ratio (voiced) - mean (s)<br>2. Spectral flux - mean (s)</td><td>1. Loudness - mean falling slope (p)<br>2. Loudness - mean rising slope (p)<br>3. F0 - std (p)</td><td>1. MFCC3 - mean (s)<br>2. F0 - percentile 50 (p)<br>3. F0 - percentile 20 (p)<br>4. F0 - percentile 80 (p)<br>5. F0 - mean (p)<br>6. Voiced segment length - mean (p)<br>7. F1 bandwidth - mean (s)</td></tr><tr><td>Happy</td><td>1. MFCC3 - mean (s)<br>2. Alpha Ratio (voiced) - mean (s)<br>3. MFCC3 (voiced) - mean (s)<br>4. F1 - mean (s)<br>5. F0 - mean (p)<br>6. F2 - mean (s)<br>7. F0 - percentile 80 (p)<br>8. F0 - percentile 50 (p)<br>9. F0 - percentile 20 (p)</td><td>1. Loudness - mean falling slope (p)<br>2. Spectral flux (voiced) - mean (s)<br>3. F0 - std (p)<br>4. Loudness - mean rising slope (p)<br>5. Slope (voiced) 500-1500 Hz - mean (s)<br>6. Jitter - std (vq)<br>7. HNR - mean (vq)</td><td>1. MFCC3 - mean (s)<br>2. F0 - percentile 20 (p)<br>3. F0 - mean (p)<br>4. Voiced segment length - std (p)<br>5. F3 amplitude - mean (s)<br>6. Spectral flux (unvoiced) - mean (s)<br>7. F0 H1-H2 - mean (s)<br>8. F0 - percentile 80 (p)</td></tr><tr><td>Sad</td><td>1. Spectral flux (voiced) - mean (s)<br>2. Spectral flux - mean (s)<br>3. Loudness - mean (p)<br>4. F0 - percentile 50 (p)<br>5. F1 - mean (s)<br>6. F0 - mean (p)<br>7. F2 - mean (s)<br>8. F0 - percentile 80 (p)<br>9. Equivalent sound level (p)<br>10. MFCC2 (voiced) - mean (s)</td><td>1. F0 - std (p)<br>2. HNR - mean (vq)<br>3. Spectral flux (voiced) - mean (s)<br>4. F0 - percentile range 0-20 (p)<br>5. F0 - percentile 80 (p)<br>6. MFCC2 (voiced) - std (s)</td><td>1. F3 amplitude - mean (s)<br>2. F0 - mean (p)<br>3. F0 H1-H2 - mean (s)<br>4. F0 - percentile range 0-20 (p)<br>5. F0 - percentile 80 (p)<br>6. F0 - std (p)<br>7. MFCC3 - mean (s)<br>8. F0 - percentile 50 (p)<br>9. F0 - percentile 20 (p)<br>10. F1 amplitude - mean (s)<br>11. Voiced segment length - mean (p)<br>12. F2 amplitude - mean (s)</td></tr><tr><td>Neutral</td><td>1. MFCC3 - mean (s)<br>2. MFCC3 (voiced) - mean (s)<br>3. F0 - mean (p)<br>4. F0 - percentile 80 (p)<br>5. F0 - percentile 20 (p)<br>6. F0 - percentile 50 (p)<br>7. MFCC3 - std (s)<br>8. Spectral flux - mean (s)</td><td>1. Loudness - mean falling slope (p)<br>2. Spectral flux (voiced) - mean (s)<br>3. F0 - std (p)<br>4. Loudness - mean rising slope (p)<br>5. F0 - percentile range 0-20 (p)<br>6. Spectral flux - mean (s)<br>7. Slope (voiced) 500-1500 - std (s)<br>8. HNR - mean (vq)<br>9. Loudness - std falling slope (p)<br>10. Jitter - std (vq)<br>11. Loudness - mean (p)</td><td>1. MFCC3 - mean (s)<br>2. Voiced segment length - mean (p)<br>3. Voiced segment length - std (p)<br>4. F0 - percentile range 0-20 (p)<br>5. F3 amplitude - mean (s)<br>6. Alpha ratio (unvoiced) - mean (s)<br>7. Shimmer - mean (vq)<br>8. F0 - std (p)<br>9. F0 - percentile 20 (p)<br>10. F0 H1-H2 - mean (s)<br>11. Hammarberg index (unvoiced) - mean (s)</td></tr></tbody></table>

Grey highlighted features correspond to unique acoustic features across groups. Standard deviation (std). Acoustic speech features are categorized as prosodic (p), voice quality (vq), and spectral (s).

when asked to do so, whereas preHD individuals showed difficulties only when instructed to narrate a happy story. Secondly, for the HD gene-carriers who followed the given instructions, the acoustic characteristics enabled to obtain satisfactory scores for SER, which demonstrates that HD mobilizes acoustic features of their voice to express these emotions. Finally, we have shown that prosodic and spectral speech features are important in the emotional speech of HD gene-carriers. Acoustic characterization of their emotional speech contributes to the understanding of their dysprosody

by further exploring the main acoustic features associated with their previously identified impaired emotional expression [12]. Overall, this lays groundwork for developing digital tools aiming to remote monitor the mental health and well-being of individuals' living with HD. In future work, further data collection and annotation will extend the scope of our study, allowing for a more comprehensive approach. Moreover, with additional data, we will be able to further investigate the impact of disease stage, and cognitive and psychiatric symptoms on the acoustic expression of emotions.
ETHICAL IMPACT STATEMENT

Our study was conducted using data obtained from partici-
pants. The conditions of our ethics approval included the ethi-
cal consent of participants and specified that the collected data
could not be publicly archived in an anonymous format. On
reasonable request, readers seeking access to the data should
contact the corresponding author. Access will be granted to
named individuals following ethical procedures for the reuse of
sensitive data including a research partnership and completion
of a data transfer partnership by the promoter of the study.

In consideration of privacy, regarding the private nature
of emotional states, participants' confidentiality and privacy
rights have been protected. Participants have not consented to
the public sharing of their emotional stories. For that reason,
all participant recordings are treated with the same level of
confidentiality as sensitive as medical and clinical records.

Furthermore, as we developed an emotion recognition
model, we included in the main text comprehensive details re-
garding the classifier used, the classified emotions, and related
information. We acknowledge the potential risks associated
with misuse or harmful applications of our model by external
parties. We aim for our work to contribute positively to
advancements in understating and supporting emotional well-
being. To promote transparency and accountability, our code
has been made available (link). Lastly, the generalizability
of our study findings remains limited to the specific HD
population.

ACKNOWLEDGMENT

We are very thankful to the patients that participated in our
study. We thank the speech therapists and the annotators for
the annotations of the speech data.

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