Cuet_Neural_Navigators@DravidianLangTech 2026: Depression Detection from Malayalam and Tamil Speech using Self-Supervised Acoustic Models

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Natural Language Processing · Depth: Expert, quick

Summary

Cuet_Neural_Navigators@DravidianLangTech 2026 introduces a system for depression detection from Malayalam and Tamil speech, addressing the challenge of early, scalable mental health screening. The system utilizes pretrained self-supervised speech encoders, including HuBERT, XLS-R, and Whisper, to identify acoustic patterns linked to depression directly from raw audio. These models are combined through ensembling to capture diverse acoustic features. Experiments involved stratified evaluation and cross-lingual analysis to assess model performance across languages. The results demonstrate that pretrained acoustic representations effectively capture vocal features associated with depression, achieving Macro-F1 scores of 0.9058 for Tamil and 0.9396 for Malayalam. However, the study notes challenges in cross-lingual transfer due to phonetic and prosodic differences between languages.

Key takeaway

For NLP Engineers developing mental health screening tools in low-resource languages, you should prioritize self-supervised acoustic models like HuBERT or Whisper. These models achieve high Macro-F1 scores (0.9058 for Tamil, 0.9396 for Malayalam) for depression detection from speech. Be aware that direct cross-lingual transfer faces challenges. This necessitates language-specific fine-tuning or model selection for optimal performance due to phonetic and prosodic variations.

Key insights

Pretrained self-supervised acoustic models effectively detect depression from raw speech in Dravidian languages, despite cross-lingual transfer challenges.

Principles

• Self-supervised acoustic models capture depression cues.
• Ensembling diverse models improves feature capture.
• Cross-lingual transfer is hindered by phonetic differences.

Method

The method combines pretrained self-supervised speech encoders (HuBERT, XLS-R, Whisper) via ensembling to identify depression-related acoustic patterns from raw audio, evaluated using stratified and cross-lingual analysis.

In practice

• Use HuBERT, XLS-R, Whisper for speech analysis.
• Ensemble models for robust acoustic feature detection.
• Consider language-specific models for Dravidian languages.

Topics

• Depression Detection
• Self-Supervised Acoustic Models
• HuBERT
• XLS-R
• Whisper
• Dravidian Languages

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.