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Towards Paradigm-General Suicide Risk Detection via Speech LLM

Conference paper on adolescent suicide-risk detection from speech. Prior speech-based assessment relies on one elicitation paradigm at a time (verbal fluency, reading, question answering); this paper unifies ten elicitation paradigms in a single speech language model using a mixture of DoRA experts that weights complementary cues across assessments. Evaluated on 1,223 participants, the model outperforms paradigm-specific and conventional joint-learning models, generalises to unseen paradigms and shows better confidence calibration.

Publisher

ISCA (Proceedings of Interspeech 2026)

Published

1 Sept 2026

Added

today

Key Findings

  • 1,223 participants across ten speech elicitation paradigms, described by the authors as the first cross-paradigm suicide-risk model
  • A mixture of DoRA experts on a speech language model beats both paradigm-specific and joint-learning baselines
  • Generalises to elicitation paradigms not seen in training
  • Improved confidence calibration relative to baselines

Methodology Notes

Five-page Interspeech paper (pages 3852 to 3856, DOI 10.21437/Interspeech.2026-666, not yet registered at Crossref on 2026-09-21). The abstract does not state the age range, the risk-label source (clinical interview or questionnaire), class balance or the size of the unseen-paradigm test; read the PDF before citing any number beyond n=1,223. Author affiliations (Tsinghua University and collaborators) inferred from the author list. Date precision: proceedings posted on the ISCA Archive between 17 and 21 September 2026 ahead of the conference (27 September to 1 October 2026); month-precision convention (day 01) used.

Authors

Li, Jialun, Jiang, Weitao, Cui, Ziyun, Duan, Yinan, Qu, Diyang, Zhang, Chao, Chen, Runsen, Lei, Chang, Wu, Wen

Tags

interspeech-2026speech-llmadolescentssuicide-riskcalibrationtsinghua

Cite This

APA

Li, Jialun et al. (2026). Towards Paradigm-General Suicide Risk Detection via Speech LLM. ISCA (Proceedings of Interspeech 2026). https://www.isca-archive.org/interspeech_2026/li26j_interspeech.html