# Childhood trauma affects speech and language measures in patients with major depressive disorder during clinical interviews

Eric Ettore, Hali Lindsay, Johannes Tröger, Michal Balazia, Benoit Michel, Philippe Robert, Danilo Postin, Rene Hurlemann, Alexandra König

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## ► To cite this version:

Eric Ettore, Hali Lindsay, Johannes Tröger, Michal Balazia, Benoit Michel, et al.. Childhood trauma affects speech and language measures in patients with major depressive disorder during clinical interviews. Journal of Affective Disorders, 2025, 388, pp.119769. 10.1016/j.jad.2025.119769. hal-05412411

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Research paper

# Childhood trauma affects speech and language measures in patients with major depressive disorder during clinical interviews

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Eric Ettore<sup>a,b,c,*</sup>, Hali Lindsay<sup>d</sup>, Johannes Tröger<sup>d</sup>, Michal Balazia<sup>e</sup>, Michel Benoit<sup>a,b</sup>,
Philippe Robert<sup>a</sup>, Danilo Postin<sup>f</sup>, Rene Hurlemann<sup>f</sup>, Alexandra König<sup>a,c,d</sup>

## ARTICLE INFO

### Keywords:

- Major depressive disorder

- Trauma

- Speech

- Language

- Speech biomarkers

- Precision psychiatry

## ABSTRACT

**Background:** Speech analysis has shown significant promise as a potential biomarker for depression. However, no studies to date have examined the impact of childhood trauma on speech and language patterns in individuals with depression. This study aims to explore the relationship between vocal characteristics and depressive symptoms, while also assessing how childhood trauma may shape these patterns.

**Methods:** 27 participants with a major depressive episode were included. The severity of depression was assessed using the Montgomery & Asberg Depression Rating Scale (MADRS) and the Beck Depression Inventory II. Childhood trauma was measured using the Childhood Trauma Questionnaire. Speech recordings from the MADRS semi-structured interview and a free clinical interview were analyzed using speaker diarization, automatic speech recognition, and feature extraction.

**Results:** Several acoustics features were significantly associated with depression severity. Correlation analysis revealed that greater depression severity was linked to shorter, less diverse speech, characterized by fewer words, fewer semantic clusters, and reduced articulatory effort. In contrast, childhood trauma was positively associated with distinct speech characteristics. Higher trauma load was associated with richer, longer, and more syntactically complex speech. Additionally, utterances were shorter, with more frequent shifts between semantic clusters, reflecting a more fragmented speech pattern influenced by traumatic load.

**Conclusion:** Our study highlights the influence of childhood trauma on vocal and linguistic characteristics of patients with depression. Automated language analysis offers the possibility to identify biomarkers of traumatic load in patients with depression. This could improve diagnostic accuracy, guide therapeutic management and monitor clinical progress.

## 1. Introduction

Major Depressive Disorder (MDD) affects over 264 million people globally, making it a leading cause of disability worldwide and a significant public health issue (GBD 2017 Disease and Injury Incidence and Prevalence Collaborators, 2018). Defined by the DSM-5 (American Psychiatric Association, 2013), MDD is a complex and heterogeneous clinical syndrome, encompassing nearly 1500 unique combinations of symptoms (Ostergaard et al., 2011).

However, depressive symptoms may overlap with or mask

underlying post-traumatic stress disorder (PTSD) in individuals with a history of trauma. In a meta-analysis, Rytwinski et al. showed that in individuals with PTSD, 52 % also meet criteria for MDD (Rytwinski et al., 2013). Other authors observed that in individuals with MDD and PTSD comorbidity, the average duration of PTSD symptoms was 16 years, yet only 28.8 % had a documented diagnosis in their medical records. This suggests that comorbidity between MDD and PTSD may predict chronic misdiagnosis of PTSD (Kostaras et al., 2017). One possible explanation for this interplay involves the fluctuating nature of post-traumatic symptoms over time (McFarlane and Papay, 1992).

<footnote><sup>a</sup> Cobtek (Cognition-Behaviour-Technology), Université Côte d'Azur, Nice, France</footnote>

<footnote><sup>b</sup> Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, Service de Psychiatrie et de Psychologie médicale, Nice, France</footnote>

<footnote><sup>c</sup> Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, Clinique Gériatrique du Cerveau et du Mouvement, Nice, France</footnote>

<footnote><sup>d</sup> ki:elements GmbH, Saarbrücken, Germany</footnote>

<footnote><sup>e</sup> INRIA (Institut national de recherche en sciences et technologies du numérique) Sophia Antipolis, France</footnote>

<footnote><sup>f</sup> Department of Psychiatry, School of Medicine and Health Sciences, University of Oldenburg, Ammerländer Heerstrasse 114-118, 26129 Oldenburg, Germany</footnote>

<footnote>* Corresponding author at: Institut Claude Pompidou, Centre Mémoire de Ressources et de Recherche, 10 Rue Molière, 06100 Nice, France.
E-mail address: ettore.e@chu-nice.fr (E. Ettore).</footnote>
According to a dynamic model proposed by Alarcon et al., these symptoms may become complicated over time by comorbid depression (Alarcon et al., 1999). So, the primary psychological trauma symptoms may recede as depressive symptoms take precedence (Auxéméry, 2015). Thus, patients presenting primarily with depressive symptoms may not be assessed for trauma histories which often remains undetected.

Large-scale studies indicate that only 30 % of individuals with MDD achieve remission following first-line antidepressant treatment, while an additional 30 % remain non-remitted even after four consecutive treatment trials (Gaynes et al., 2009). One of the potential explanations for these finding is that individuals with undiagnosed PTSD are less likely to respond adequately to standard antidepressant treatments and may require trauma-focused interventions to achieve significant clinical improvement (Nanni et al., 2012). Early detection of trauma history is therefore critical, as it enables the implementation of trauma-focused therapies, which have demonstrated superior outcomes in patients with comorbid depressive and post-traumatic symptoms (Watts et al., 2013).

Recent years have witnessed a proliferation of digital biomarkers aimed at objectively assessing behavioral and biological indicators of psychiatric conditions (Jacobson et al., 2019; Schultebraucks et al., 2021). Among these, speech analysis presents a compelling opportunity to investigate disease-related characteristics, as psychiatric symptoms often manifest through alterations in speech and language. This approach facilitates clinically applicable assessments of speech patterns, encompassing aspects such as speed, coherence, and content across various psychiatric conditions, including depression, schizophrenia, and PTSD (de Boer et al., 2023; Koops et al., 2023; Marmar et al., 2019). Advancements in computational linguistics, natural language processing, and speech recognition enhance the feasibility of using automatic speech analysis as an objective clinical measure of psychiatric symptoms (König et al., 2022a).

Specifically, speech has emerged as a potential target within the realm of precision psychiatry, signaling clinically relevant phenomena such as self-harm, suicidal behavior, substance abuse, depression, and disease recurrence (Kappen et al., 2023). Consistent findings in depression research indicate prosodic, and spectral features associated with MDD, including monotonic and flat speech patterns attributed to reductions in fundamental frequency (f0) and f0 range (Horwitz et al., 2013; Kiss and Vicsi, 2017). Moreover, patients with MDD often display a reduced speech rate and utterance duration, likely linked to psychomotor retardation (Mundt et al., 2012; Yamamoto et al., 2020). This association has been noted concerning both the severity of depression and treatment response, particularly for temporal features such as pause duration and speaking rate (Mundt et al., 2012). Additionally, deviations in voice quality markers, such as jitter and shimmer, as well as spectral features, have been documented in individuals with depression (Alghowinem et al., 2013; Cummins et al., 2015; Taguchi et al., 2018; Wang et al., 2019).

Linguistic changes, including heightened self-referential speech and increased use of past tense verbs, have also been observed in depressed individuals (Iuliu Hatieganu et al., 2017; Koops et al., 2023). Furthermore, those with depression tend to convey more emotionally negative content while utilizing less complex vocabulary (Arevian et al., 2020; Shinohara et al., 2021).

Voice analysis in individuals with a history of psychotrauma represents an emerging field with promising potential. Research indicates that subjects with PTSD typically exhibit a more monotonic voice characterized by fewer pitch variations (Marmar et al., 2019), decreased intensity (Schultebraucks et al., 2021), and slower speech (Marmar et al., 2019; Scherer et al., 2016). Similar to findings in depression, parameters such as F0 and source indicators (e.g., jitter, shimmer, and harmonics-to-noise ratio) are also affected (Scherer et al., 2013; R. Xu et al., 2012).

Studies focusing on childhood trauma, measured through the Childhood Trauma Questionnaire (CTQ), have demonstrated a

correlation between F0 and the severity of traumatic experiences. Specifically, these studies revealed alterations in voice during trauma recall, characterized by irregular voice patterns (Monti et al., 2021). Other research has identified increased speech hesitance and changes in narrative coherence during trauma recollection (He et al., 2017; Huon and Stutz, 2020).

To the authors' knowledge, no study has examined how childhood trauma measured by CTQ can affect speech in depressed people. The assessment of speech, language, and interaction features presents a non-invasive, objective, and quantifiable method for evaluating depressive symptoms. This approach paves the way for personalized diagnoses and more precise follow-up, enhancing sensitivity to subtle emotional state variations and facilitating early relapse detection. Additionally, voice analysis may serve as a measurable indicator for identifying and monitoring psychotrauma, thereby guiding therapeutic interventions (Gumus et al., 2023).

We used data collected in the 'MEPHESTO' study, a multi-center longitudinal corpus project aimed at developing and validating new multimodal markers for several psychiatric disorders. 'MEPHESTO' consists of recordings multimodal audio, video and physiological measures during clinical interviews, as well as clinical assessments. During the recordings, clinicians are also recorded using the same modalities as the participants (König et al., 2022).

The primary aim of this study is to investigate the relationship between speech and linguistic features automatically extracted from clinical interviews and depression symptoms, ultimately contributing to accurate staging of severity. Furthermore, the study aims to examine the influence of childhood trauma on these vocal markers.

## 2. Method

### 2.1. Participants

Participants were recruited from the psychiatric clinic at the University Hospital in Nice, France. Recruitment focused on both inpatients and outpatients diagnosed with depression. All patients recruited were treated pharmacologically.

### 2.2. Inclusion criteria

- A diagnosis of Major Depressive Episode according to DSM-5 criteria

- Aged between 16 and 60

- Have capacity to consent

### 2.3. Non-Inclusion criteria

- Not native speakers or had major hearing or language problems

- Disabling or non-stabilized somatic pathology

- Major neurocognitive disorder or diagnosis of neurocognitive pathology

- History likely to modify the cerebral anatomy (for example brain tumor or stroke)

- Bipolar disorder, Schizophrenia, Obsessive compulsive disorder, Attention deficit hyperactivity disorder

- Current diagnosis of substance abuse or dependency except tobacco

### 2.4. Setting

The work presented in this paper is part of a larger study called 'MEPHESTO', a joint program between INRIA, the French national institute for the digital sciences, and DFKI, the German Research center for Artificial Intelligence. The aim of the 'MEPHESTO' study is to perform a longitudinal, observational, and multicentric study in psychiatric clinics in Germany and France (König et al., 2022). This paper presents results from the French cohort.
Ethical approval has been granted by the Ethics Committee “Comité de protection des personnes” (CPP Ouest II in Angers, France). The study was conducted in accordance with the principles outlined in the Declaration of Helsinki. Written informed consent has been obtained from all subjects prior to their inclusion in the study.

## 2.5. Clinical assessment and procedure

For recruitment, an initial screening visit was carried out. Patients received a detailed information sheet about the study, its process and potential risks and benefits. If they met the inclusion criteria, patients were included after signing the consent form.

The 'MEPHESTO' study consists of weekly visits over six weeks followed by several recordings over twelve months (König et al., 2022). This study focuses on results from the first two visits.

The first visit referred to as "V0" served as the baseline assessment, while the second visit, "V1" was conducted one week later and involved a free clinical interview.

More precisely, the first visit, V0, was structured as follows:

- Clinical interview:

* Demographics data (age, sex, education, living status)

* Medical and psychiatric history

* Current medication collection

- Psychometric assessments clinician administrated:

* Mini International Neuropsychiatric Interview (MINI) for DSM 5 diagnosis (American Psychiatric Association, 2013)

* Montgomery Asberg Depression Rating Scale (MADRS) (Montgomery and Åsberg, 1979)

- Self-reported assessments:

* Beck Depression Inventory II (BDI-II) (Beck et al., 2011)

* Childhood Trauma Questionnaire (Bernstein et al., 2011)

The V1 visit consisted of a free-format clinical interview. Participants were encouraged to discuss any topic of their choice, allowing for natural, unconstrained speech to be recorded for further analysis. So, each of these visits began by explaining that it was a totally free interview, and then asking the question: "What would you like to talk about today?"

## 2.6. Interpretation of psychometric assessments

The MADRS is ranging from 0 to 60. Total scores are interpreted as follows: 0-6 indicates no depression, 7-19 mild, 20-34 moderate, and ≥ 35 severe depression (Montgomery and Åsberg, 1979).

The BDI-II is ranging from 0 to 63, with cut-offs defined as: 0-13 minimal, 14-19 mild, 20-28 moderate, and 29-63 severe depressive symptoms (Beck et al., 2011).

The CTQ includes five subscales: emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect. Each subscale score ranges from 5 to 25, with established clinical thresholds for moderate to severe trauma as follows: emotional abuse ≥13, physical abuse ≥10, sexual abuse ≥8, emotional neglect ≥15, and physical neglect ≥10 (Bernstein et al., 2011).

## 2.7. Technical setup and audio recording

The recording setup was intentionally designed to be minimal and non-invasive, ensuring natural behavior during interviews. The recordings were conducted in a hospital consultation room, providing a relatively quiet environment. All recordings took place in the same room to maintain consistency. During the interviews the patient and clinician sat opposite each other, approximately 1.5 to 2 m apart, with a table between them. A small, discreet microphone was placed on the table between the patient and clinician to capture their speech. The conversations were recorded as audio files. Automatic speech recognition was

applied to these files, followed by manual verification to ensure accuracy. The recorded data were securely stored on a protected server.

## 2.8. Speech and language processing

To compute the acoustic and linguistic features, the extraction scripts were implemented in Python 3.9, based on ki: elements' proprietary speech processing library "Sigma" (Tröger et al., 2022). The extraction code is available upon reasonable request. Transcripts were obtained using Amazon Web Services automatic speech recognition and speaker diarization. A labeled transcript was created with time stamps and a speaker label for each exchange in the conversation.

To analyze speech patterns in clinical interactions, we investigated a comprehensive set of features previously associated with symptoms of depression including 56 acoustic features and 42 linguistic features (König et al., 2022b; Menne et al., 2024).

Acoustic features included Mel-Frequency Cepstral Coefficients (MFCCs) to capture sound quality, shimmer and jitter to measure voice stability, and formant characteristics to analyze vowel sounds. Additionally, pitch-related features such as pitch range and standard deviation, along with loudness and spectral features, were calculated to assess voice clarity and energy distribution. Prosodic features focused on capturing the duration of speech, pauses, and patterns of voiced and unvoiced segments. Pitch and loudness dynamics were also measured to evaluate vocal variation.

Linguistic features assessed vocabulary richness, sentence complexity, and part-of-speech usage through measures like type-token ratio and Honore's statistic, alongside analyses of word usage patterns and syntactic phrase structures.

In addition, mean latency was used to quantify the response time between conversational turns in the clinician-patient dialogues. This directional feature captures the mean response time in seconds separately for the clinician responding to the patient (clinician response time) and the patient responding to the clinician (patient response time). Response times were computed as the difference between the start time of the current speaker's utterance and the end time of the preceding speaker's utterance. For each direction, an average of all response latencies was calculated.

## 2.9. Statistical analysis

Statistical analyses were performed using Jamovi software (The jamovi project, 2024). Due to the small sample size, Spearman's rank correlation tests were used for calculating correlations. For the V0 visit, statistical analyses were performed exclusively on data collected during the semi-structured MADRS interview segment that was manually segmented and cut. In contrast, for the V1 visit, the entire free interview was included in the analysis.

# 3. Results

## 3.1. Demographics

A total of 27 participants diagnosed with MDD were recruited, with a mean age of 45.4 ± 10.9 years. The sample comprised 19 female and 8 male participants. The mean score on the Montgomery-Åsberg Depression Rating Scale (MADRS) was 33.5 ± 10.7, the mean score on the Beck Depression Inventory II (BDI-II) was 32.5 ± 13.4 and the mean score on the Childhood Trauma Questionnaire (CTQ) was 57 ± 15.1.

MADRS semi-structured scale scores showed a significant correlation with BDI self-reported scores (Rho Spearman = 0.48 with p = 0.012).

## 3.2. Correlation analysis between voice parameters and BDI-II scores

During the V0 semi-structured interview, several filter, source and spectral parameters exhibited negative correlations with depression
average cluster size and type-token ratio points to a tendency toward repetition or lower cohesion within thematic units, while the use of lower-frequency words may reflect a shift toward more abstract or less commonly used vocabulary.

Analysis of the V1 interview (Table 2.2) aligned with the V0 findings, demonstrating that greater traumatic load was negatively correlated with both the mean cluster size and its standard deviation, suggesting a tendency toward smaller and more uniform lexical-semantic groupings. This pattern may indicate reduced thematic coherence or increased fragmentation in discourse. Furthermore, positive correlations were observed with the rate of adjective use, and the number of consecutive repetitions.

### 3.4. Diverging vocal and linguistic profiles associated with depression severity and childhood trauma load

A comparative analysis of correlations with BDI-II and CTQ scores revealed contrasting patterns in both vocal and linguistic features.

Higher BDI-II scores were associated with a consistent reduction in vocal variability, including negative correlations with spectral and articulatory parameters such as F2 and F3 frequency and bandwidth variability, H1/H3 harmonic difference, and prosodic measures like speech duration and voiced segment variability. These findings reflect a vocal profile characterized by monotonous prosody, diminished articulatory flexibility, and flattened affect. Notably, negative correlation with spectral features such as the alpha ratio standard deviation and the spectral slope between 500 and 1500 Hz, reinforcing the interpretation of spectral flattening and reduced prosodic modulation in depressive speech.

Linguistically, depression was linked to simplified syntactic structure, lower lexical diversity, and a greater reliance on frequent, high-usage words, indicating a general flattening affective expression.

In contrast, higher CTQ scores correlated with a pattern of increased linguistic complexity, including greater speech duration, more extensive syntactic dependency distances, and a higher number and density of lexical-semantic clusters. However, these features were accompanied by smaller cluster sizes, a lower type-token ratio, and increased repetition, suggesting that while CTQ-related speech may be more elaborated, it is also more fragmented and less cohesive.

Moreover, CTQ scores showed positive correlations with spectral parameters, specifically the standard deviation of the alpha ratio and the spectral slope between 500 and 1500 Hz. Interestingly, these same spectral features were negatively correlated with BDI-II scores, suggesting that while depression is characterized by spectral flattening and vocal suppression, childhood trauma may lead to greater spectral instability or fluctuation.

## 4. Discussion

The present study investigated the impact of childhood trauma on speech and linguistic features in patients with MDD during clinical interviews. Our findings highlight the complex interplay between trauma, depression, and speech patterns, offering valuable insights into how childhood experiences shape vocal expression in individuals with MDD.

### 4.1. Depression severity and speech features

First, our results demonstrated that speech features were strongly influenced by the severity of depression. We found that severely depressed patients had shorter speech durations, which were negatively correlated with symptom severity. Literature seems to point in this direction, showing a drop in the speech rate in depressed patients (Hönig et al., 2014; Horwitz et al., 2013).

Moreover, Higher BDI-II scores were associated with less variation in both spectral and articulatory features, including reduced variability in formant frequencies (F2, F3), harmonic differences (H1/H3), and

prosodic aspects such as speech duration and voiced segment length. These results are consistent with prior studies showing that individuals with major depressive disorder tend to speak with lower pitch variation and reduced vocal expressiveness (X. Xu et al., 2023).

We did not find any significant results for fundamental frequency (F0) for which several studies have found a lower F0 in depression (Hönig et al., 2014; Horwitz et al., 2013; Yamamoto et al., 2020). However, other studies had not found this drop in F0 in depressed subjects (Mundt et al., 2012; Taguchi et al., 2018). Given that 70 % of our cohort were women, a potential explanation could be a gender effect on F0. In addition, we had no control group, and the participants' treatments may have influenced this feature.

We found an inverse correlation between spectral energy measured by mel-frequency cepstral coefficients (MFCCs) and the severity of depression in our free interview. We found this correlation specifically with MFCC2. This was consistent with the literature (Hönig et al., 2014). Other studies have shown inverse correlations between symptom severity and other MFCCs (Taguchi et al., 2018; Wang et al., 2019). We also found a positive correlation with MFCC1, indicating increased vocal constriction or reduced spectral openness, which is not opposed to the negative correlation observed with MFCC2, reflecting decreased articulatory variation. Together, these features suggest a more constrained and less dynamic vocal profile in individuals with higher depression severity.

No significant differences were found with jitter and shimmer, in contrast to other studies (Horwitz et al., 2013; Quatieri and Malyska, 2012).

Linguistically, higher depression severity was linked to simplified grammatical structures, decreased lexical diversity, and a preference for frequent, common words, reflecting a general flattening of affect expressiveness.

Finally, depressive speech patterns are more pronounced in free-format interviews, which might be due to the increased demand for spontaneous and self-organized speech production.

### 4.2. Influence of childhood trauma on speech features

Our analysis revealed that higher scores on the CTQ were associated with increased vocal and syntactic complexity, as well as greater fragmentation.

Our results indicate that increased traumatic burden correlates with total speech duration, syntactic dependency distance, and the number and density of lexical-semantic clusters, suggesting a longer speech duration and a discourse structure that is more segmented and syntactically complex. Additionally, CTQ scores were positively associated with adjective usage and consecutive word repetitions, indicating a speech pattern marked by evaluative language and lexical redundancy.

Studies have shown that traumatic narratives can be characterized by a richness in sensory or emotional details and a lexicon centered around themes of death or bodily sensations (Auxéméry and Gayraud, 2021; Crespo and Fernández-Lansac, 2016; Huon and Stutz, 2020; Kleim et al., 2018; O'Kearney and Perrott, 2006).

In our study, we did not explicitly prompt participants to recount their traumatic events; these themes emerged spontaneously during speech. Furthermore, we did not precisely analyze the content of the discourse, which complicates the interpretation of the results. However, this suggests that our results may not be exclusively limited to the reporting of traumatic content.

Our additional findings revealed that subjects with high traumatic load produced shorter utterances and exhibited smaller, more numerous semantic clusters that were frequently modified. This pattern may suggest a tendency toward more fragmented speech.

Several studies have shown that traumatic narratives may feature disruptions in chronology, incomplete sentences, repetitions, or a loss of coherence, with abrupt shifts between different moments of the experience (Auxéméry and Gayraud, 2021; Crespo and Fernández-Lansac,
2016; Huon and Stutz, 2020).

One possible explanation could be the presence of dissociation. Indeed, studies of individuals with the dissociative subtype of PTSD have shown that their narrative accounts are more fragmented, with abrupt changes of subject, incomplete sentences and breaks in chronology (Misitano et al., 2024). In addition, these patients seem to have a tendency toward attenuated emotional expression or even affective detachment when recounting their traumatic experiences, which could influence the content of the discourse (Lanius et al., 2012; Misitano et al., 2024).

People with the dissociative subtype of PTSD may also have episodic memory impairments, which may influence the coherence and organization of the traumatic narrative (Petzold and Bunzeck, 2022). These disturbances in language and emotional expression are probably linked to disrupted neural connectivity (Lanius et al., 2012).

Otherwise, dissociative symptoms are found to co-occur with depressive symptoms (Fung et al., 2020) but also to be longitudinally correlated with depressive symptoms. Thus, Fung et al. assumed that post-traumatic and dissociative symptoms could be a contributing factor for depressive symptoms (Fung et al., 2023).

We found neither significant relationship between fundamental frequency and CTQ traumatic load (Monti et al., 2021) nor changes in the jitter, shimmer or HNR parameters that have been found in studies targeting PTSD (Scherer et al., 2013; R. Xu et al., 2012).

### 4.3. Implications for clinicians

Our results revealed opposing trends. Depression severity was linked to shorter, less diverse speech, characterized by fewer words, fewer semantic clusters, and reduced articulatory effort. While, higher trauma load was associated with richer, longer, and more complex speech. Utterances were shorter, with more frequent shifts between semantic clusters and more consecutive repetitions. These differences suggest distinct speech patterns underlying the influence of trauma load on depression that could be used as potential biomarkers.

The implications of these findings are significant for clinical practice. Given that speech and language features can serve as important biomarkers for depression, integrating automatic speech analysis into clinical assessments may enhance diagnostic accuracy and inform treatment decisions. These depression biomarkers could potentially be utilized for broader screening, such as voice analysis during consultations with medical or paramedical professionals without specialized psychiatric training. In the long term, they may also serve as valuable tools for monitoring treatment response or as early indicators for identifying relapse.

Recognizing the nuanced impact of trauma on the speech patterns of depressed patients could enable clinicians to better personalize their therapeutic approaches (Lenjalley, 2023). Voice biomarkers indicating a high traumatic load in depressed individuals could have significant implications. Firstly, they could facilitate early identification of traumatic experiences, which are often challenging to uncover during initial consultations. Additionally, the interplay between childhood trauma and depression highlights the importance of incorporating comprehensive trauma histories into MDD diagnoses. Secondly, these biomarkers could inform treatment adjustments, such as the inclusion of trauma-focused psychotherapies like Prolonged Exposure Therapy (PET), Cognitive Processing Therapy (CPT), Written Exposure Therapy (WET) or EMDR, to address the specific needs of these patients more effectively. Several studies have demonstrated efficacy of these therapies in treating PTSD and comorbid depressive symptoms (Dominguez et al., 2021; Eftekhari et al., 2013; Resick et al., 2008; Sephry et al., 2021; Sloan et al., 2013; Yan et al., 2021). Finally, we could also imagine that these vocal biomarkers could serve as a marker of response to trauma treatment. We might consider that the normalization of trauma-related vocal impairment could be synonymous with trauma integration.

The links between depression and childhood trauma are complex and

frequently found to be comorbid (Warembourg et al., 2018). The relationship between trauma and depression may be bidirectional, but several studies have shown a higher risk of depression in cases of childhood trauma (Maniglio, 2010; Teicher et al., 2009; Vibhakar et al., 2019). Clinicians need to be aware that trauma can influence the expression of typical depressive symptoms and conversely, depression can sometimes mask the presence of trauma. This can lead to trauma being under- or misdiagnosed (Breslau, 1991; Meltzer et al., 2012). Therefore, an integrated approach combining traditional assessment tools and advanced speech analysis could enable a more holistic understanding of a patient's mental health status.

### 4.4. Limitations and perspectives

While this study provides valuable insights, it is not without limitations. The sample size, although sufficient for preliminary findings, is relatively small, which may limit the generalizability of the results. Future research should aim to replicate these findings in larger and more diverse populations to enhance external validity. Additionally, longitudinal studies are warranted to explore how speech characteristics evolve over time in relation to trauma and depression severity.

A key methodological limitation of this study lies in the absence of mutual statistical adjustment in the correlation analyses. Specifically, associations between vocal parameters and childhood trauma (CTQ) scores were not adjusted for current depressive symptom severity (BDI), and vice versa. Failing to control for the non-target variable increases the risk of confounding, thereby limiting causal inference and the attribution of voice-related changes to specific underlying mechanisms.

This methodological shortcoming limits the interpretability of the observed associations. Without controlling for overlapping variance, it remains unclear whether the vocal alterations reflect trauma-specific processes, affective symptoms, or shared variance between the two constructs. Future studies should implement partial correlation analyses or multivariate regression models to isolate the unique contributions of trauma and depression. This would allow for a more precise identification of voice-based digital biomarkers that differentiate trauma-related and affective vocal signatures.

Another limitation of our study is that we did not systematically find the same significant results depending on whether the analysis targeted the V0 semi-structured interview or the V1 free interview. There are several possible explanations for this, in particular the small sample size, the fact that treatments may have changed between the two interviews, and the fact that symptoms may also have improved or worsened. Nevertheless, these differences raise an important question. Indeed, the free interview seems to allow easier access to a discourse initiated spontaneously by the participant and less in response to a question from the clinician. This seems to give access to parameters linked to the content of the discourse and the way it is organized. It therefore appears necessary for automated language analysis to take this into account. To make these vocal biomarkers even more precise, it would be interesting to combine them with markers for analyzing emotions and non-verbal behavior such as video or physiological sensing. As mentioned above, language impairment is also suggested to be linked to impairment of emotional expression (Lanius et al., 2012; Misitano et al., 2024).

Another avenue for future research includes examining the neurobiological mechanisms underlying the observed speech patterns in trauma-exposed individuals with depression. Understanding these mechanisms could inform the development of targeted interventions that address the root causes of altered speech and language expression in this population.

## 5. Conclusion

This study highlights the significant influence of childhood trauma on speech and linguistic features in patients with MDD. The findings suggest that trauma history can alter the way individuals express
themselves verbally, impacting both the content and delivery of speech. By advancing our understanding of these relationships, we can improve diagnostic practices and therapeutic strategies, ultimately enhancing care for individuals affected by MDD.

## CRediT authorship contribution statement

**Eric Ettore:** Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Formal analysis, Conceptualization. **Hali Lindsay:** Writing – review & editing, Visualization, Validation, Supervision, Software, Methodology, Formal analysis, Data curation, Conceptualization. **Johannes Tröger:** Writing – review & editing, Visualization, Validation, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Michal Balazia:** Writing – review & editing, Validation, Software, Data curation. **Michel Benoit:** Writing – review & editing, Conceptualization. **Philippe Robert:** Writing – review & editing, Visualization, Validation, Supervision, Project administration, Methodology, Investigation, Conceptualization. **Danilo Postin:** Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. **Rene Hurlemann:** Writing – review & editing, Validation, Conceptualization. **Alexandra König:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

## Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used Chat GPT in order to improve language and readability. After using this tool, the authors reviewed and edited the content as needed and takes full responsibility for the content of the publication.

## Funding source

This research was funded by the MEPHESTO project (BMBF Grant Number 011S20075; DRI-0120105/291).

## Declaration of competing interest

The Authors declare no Competing Financial or Non-Financial Interests.

## Acknowledgments

We would like to thank the following people for their help in carrying out our study and processing the data: Valeria Manera, PhD and Alexandre Derreumaux.

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