Leveraging Large Language Models for Spontaneous Speech-Based Suicide Risk Detection
This addresses suicide prevention in adolescents with a non-invasive speech-based method, though it appears incremental as it combines existing LLM features with conventional ones.
The paper tackled suicide risk detection in adolescents using speech analysis, achieving 74% accuracy on a test set and ranking first in the SpeechWellness Challenge.
Early identification of suicide risk is crucial for preventing suicidal behaviors. As a result, the identification and study of patterns and markers related to suicide risk have become a key focus of current research. In this paper, we present the results of our work in the 1st SpeechWellness Challenge (SW1), which aims to explore speech as a non-invasive and easily accessible mental health indicator for identifying adolescents at risk of suicide.Our approach leverages large language model (LLM) as the primary tool for feature extraction, alongside conventional acoustic and semantic features. The proposed method achieves an accuracy of 74\% on the test set, ranking first in the SW1 challenge. These findings demonstrate the potential of LLM-based methods for analyzing speech in the context of suicide risk assessment.