Spontaneous Speech-Based Suicide Risk Detection Using Whisper and Large Language Models
This work addresses early suicide risk detection for adolescents, which is an incremental improvement using existing models on new data.
The paper tackles automatic suicide risk detection in adolescents using spontaneous speech, achieving a detection accuracy of 0.807 and an F1-score of 0.846 on a test set with 119 subjects.
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.