  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.
