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Spontaneous Speech-Based Suicide Risk Detection Using Whisper and Large Language Models

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arxiv 2406.03882 v2 pith:EY5NPSX2 submitted 2024-06-06 cs.CL cs.SDeess.AS

Ziyun Cui , Chang Lei , Wen Wu , Yinan Duan , Diyang Qu , Ji Wu , Runsen Chen , Chao Zhang This is my paper
classification cs.CLcs.SDeess.AS
keywords suicidedetectionriskspeechmodelsspontaneouswhisperadolescents
verification ladder T0 review T1 audit T2 compute T3 formal
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The early detection of suicide risk is important since it enables the intervention to prevent potential suicide attempts. This paper studies the automatic detection of suicide risk based on spontaneous speech from adolescents, and collects a Mandarin dataset with 15 hours of suicide speech from more than a thousand adolescents aged from ten to eighteen for our experiments. To leverage the diverse acoustic and linguistic features embedded in spontaneous speech, both the Whisper speech model and textual large language models (LLMs) are used for suicide risk detection. Both all-parameter finetuning and parameter-efficient finetuning approaches are used to adapt the pre-trained models for suicide risk detection, and multiple audio-text fusion approaches are evaluated to combine the representations of Whisper and the LLM. The proposed system achieves a detection accuracy of 0.807 and an F1-score of 0.846 on the test set with 119 subjects, indicating promising potential for real suicide risk detection applications.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Leveraging Large Language Models for Spontaneous Speech-Based Suicide Risk Detection

    cs.SD 2025-07 conditional novelty 5.0 of 10

    A multimodal system combining acoustic, textual, and LLM-extracted features reports 74% accuracy for adolescent suicide risk detection on the SW1 challenge test set.

  2. Language-Agnostic Suicidal Risk Detection Using Large Language Models

    cs.CL 2025-05 conditional novelty 4.0 of 10

    LLM-generated English summaries of Chinese speech can train English models to predict suicidal risk about as well as Chinese models trained on Chinese summaries, at least on the development set.

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