Author

Yuezhou Zhang

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Open access Sep 2026

Multilingual lexical feature analysis of spoken language for predicting major depression symptom severity.

BACKGROUND Remotely captured spoken language could provide objective, regular indicators of depression symptom severity. However, research to date has largely used non-clinical, cross-sectional written language and complex machine learning (ML) approaches with limited interpretability. METHODS We used linear mixed-ef...

A. Tokareva, J. Dineley, Z. Firth et al. · Journal of Affective Disorde... · 0 citations

#machine learning Nov 2025

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

Depression symptom severity was associated with five lexical features, including reductions in word count measures, use of first-person plural pronouns and positive word frequency, andLexical features and vector embeddings did improve prediction accuracy beyond baseline models.

A. Tokareva, J. Dineley, Z. Firth et al. · arXiv.org · 0 citations