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

TL;DR

This paper demonstrates that large language models can effectively analyze speech to identify adolescents at risk of suicide, achieving high accuracy in a competitive challenge and highlighting their potential in mental health assessment.

Contribution

The study introduces a novel approach combining large language models with acoustic and semantic features for suicide risk detection from speech.

Findings

Achieved 74% accuracy on test data

Ranked first in the SW1 challenge

Shows potential of LLMs in mental health analysis

Abstract

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.