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

2025/07/01 by Yifan Gao, Jiao Fu, Gao, Yifan +5
Psychology · #Audio and Speech Processing (eess.AS) #Computation and Language (cs.CL) #Emotion and Mood Recognition #FOS: Computer and information sciences #FOS: Electrical engineering #Mental Health via Writing #Sound (cs.SD) #Suicide and Self-Harm Studies #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2507.00693

openalex publication_date 2025/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Related

• In-context learning capabilities of Large Language Models to detect suicide risk among adolescents from speech transcripts
• Towards Cross-Task Suicide Risk Detection via Speech LLM
• The 1st SpeechWellness Challenge: Detecting Suicide Risk Among Adolescents
• Language-Agnostic Suicidal Risk Detection Using Large Language Models
• Suicide Risk Assessment Using Multimodal Speech Features: A Study on the SW1 Challenge Dataset