Multilingual Lexical Feature Analysis of Spoken Language for Predicting Major Depression Symptom Severity

• URL: http://arxiv.org/abs/2511.07011v1
• Date: Mon, 10 Nov 2025 12:03:16 GMT
• Title: Multilingual Lexical Feature Analysis of Spoken Language for Predicting Major Depression Symptom Severity
• Authors: Anastasiia Tokareva, Judith Dineley, Zoe Firth, Pauline Conde, Faith Matcham, Sara Siddi, Femke Lamers, Ewan Carr, Carolin Oetzmann, Daniel Leightley, Yuezhou Zhang, Amos A. Folarin, Josep Maria Haro, Brenda W. J. H. Penninx, Raquel Bailon, Srinivasan Vairavan, Til Wykes, Richard J. B. Dobson, Vaibhav A. Narayan, Matthew Hotopf, Nicholas Cummins, The RADAR-CNS Consortium,
• Abstract summary: We describe an initial exploratory analysis of longitudinal speech data and PHQ-8 assessments from 5,836 recordings of 586 participants in the UK, Netherlands, and Spain.<n>We sought to identify interpretable lexical features associated with MDD symptom severity with linear mixed-effects modelling.<n>In English, MDD symptom severity was associated with 7 features including lexical diversity measures and absolutist language.<n>In Dutch, associations were observed with words per sentence and positive word frequency; no associations were observed in recordings collected in Spain.
• Score: 5.950020142175479
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Background: Captured between clinical appointments using mobile devices, spoken language has potential for objective, more regular assessment of symptom severity and earlier detection of relapse in major depressive disorder. However, research to date has largely been in non-clinical cross-sectional samples of written language using complex machine learning (ML) approaches with limited interpretability. Methods: We describe an initial exploratory analysis of longitudinal speech data and PHQ-8 assessments from 5,836 recordings of 586 participants in the UK, Netherlands, and Spain, collected in the RADAR-MDD study. We sought to identify interpretable lexical features associated with MDD symptom severity with linear mixed-effects modelling. Interpretable features and high-dimensional vector embeddings were also used to test the prediction performance of four regressor ML models. Results: In English data, MDD symptom severity was associated with 7 features including lexical diversity measures and absolutist language. In Dutch, associations were observed with words per sentence and positive word frequency; no associations were observed in recordings collected in Spain. The predictive power of lexical features and vector embeddings was near chance level across all languages. Limitations: Smaller samples in non-English speech and methodological choices, such as the elicitation prompt, may have also limited the effect sizes observable. A lack of NLP tools in languages other than English restricted our feature choice. Conclusion: To understand the value of lexical markers in clinical research and practice, further research is needed in larger samples across several languages using improved protocols, and ML models that account for within- and between-individual variations in language.

Related papers

• Linguistic and Audio Embedding-Based Machine Learning for Alzheimer's Dementia and Mild Cognitive Impairment Detection: Insights from the PROCESS Challenge [0.0]
  Speech, encompassing both acoustic and linguistic dimensions, offers a promising non-invasive biomarker for cognitive decline.<n>We present a machine learning framework for the PROCESS Challenge, leveraging both audio embeddings and linguistic features derived from spontaneous speech recordings.
  arXiv  Detail & Related papers  (2025-10-02T06:54:55Z)
• The Emergence of Abstract Thought in Large Language Models Beyond Any Language [95.50197866832772]
  Large language models (LLMs) function effectively across a diverse range of languages.<n>Preliminary studies observe that the hidden activations of LLMs often resemble English, even when responding to non-English prompts.<n>Recent results show strong multilingual performance, even surpassing English performance on specific tasks in other languages.
  arXiv  Detail & Related papers  (2025-06-11T16:00:54Z)
• Bigger But Not Better: Small Neural Language Models Outperform Large Language Models in Detection of Thought Disorder [7.585589727435719]
  We investigate whether smaller neural language models can serve as effective alternatives for detecting positive formal thought disorder.<n>Surprisingly, our results show that smaller models are more sensitive to linguistic differences associated with formal thought disorder than their larger counterparts.
  arXiv  Detail & Related papers  (2025-03-25T22:55:58Z)
• A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context [0.9074663948713616]
  Mental health disorders pose a growing public health concern in the Arab world.<n>This study comprehensively evaluates 8 large language models (LLMs) on diverse mental health datasets.
  arXiv  Detail & Related papers  (2025-01-12T16:17:25Z)
• LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment [75.44934940580112]
  This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment.<n>We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews.<n>Our approach, tested on 236 real-world interviews, demonstrates strong correlations with clinician assessments.
  arXiv  Detail & Related papers  (2025-01-07T08:49:04Z)
• Building Multilingual Datasets for Predicting Mental Health Severity through LLMs: Prospects and Challenges [3.0382033111760585]
  Large Language Models (LLMs) are increasingly being integrated into various medical fields, including mental health support systems.<n>We present a novel multilingual adaptation of widely-used mental health datasets, translated from English into six languages.<n>This dataset enables a comprehensive evaluation of LLM performance in detecting mental health conditions and assessing their severity across multiple languages.
  arXiv  Detail & Related papers  (2024-09-25T22:14:34Z)
• Comparing Hallucination Detection Metrics for Multilingual Generation [62.97224994631494]
  This paper assesses how well various factual hallucination detection metrics identify hallucinations in generated biographical summaries across languages. We compare how well automatic metrics correlate to each other and whether they agree with human judgments of factuality. Our analysis reveals that while the lexical metrics are ineffective, NLI-based metrics perform well, correlating with human annotations in many settings and often outperforming supervised models.
  arXiv  Detail & Related papers  (2024-02-16T08:10:34Z)
• Quantifying the Dialect Gap and its Correlates Across Languages [69.18461982439031]
  This work will lay the foundation for furthering the field of dialectal NLP by laying out evident disparities and identifying possible pathways for addressing them through mindful data collection.
  arXiv  Detail & Related papers  (2023-10-23T17:42:01Z)
• Evaluating Large Language Models for Radiology Natural Language Processing [68.98847776913381]
  The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP) This study seeks to bridge this gap by critically evaluating thirty two LLMs in interpreting radiology reports.
  arXiv  Detail & Related papers  (2023-07-25T17:57:18Z)
• Semantic Coherence Markers for the Early Diagnosis of the Alzheimer Disease [0.0]
  Perplexity was originally conceived as an information-theoretic measure to assess how much a given language model is suited to predict a text sequence. We employed language models as diverse as N-grams, from 2-grams to 5-grams, and GPT-2, a transformer-based language model. Best performing models achieved full accuracy and F-score (1.00 in both precision/specificity and recall/sensitivity) in categorizing subjects from both the AD class and control subjects.
  arXiv  Detail & Related papers  (2023-02-02T11:40:16Z)
• Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts [56.72488923420374]
  Pre-trained language models (LMs) have shown great potential for cross-lingual transfer in low-resource settings. We show the few-shot cross-lingual transfer property of LMs for named recognition (NER) and apply it to solve a low-resource and real-world challenge of code-mixed (Spanish-Catalan) clinical notes de-identification in the stroke.
  arXiv  Detail & Related papers  (2022-04-10T21:46:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.