# The 1st SpeechWellness Challenge: Detecting Suicide Risk Among Adolescents

arXiv: https://arxiv.org/abs/2501.06474 · HTML: https://mlsanity.com/paper/2501.06474/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2025.

## Built on by (most cited first, 6 of 6)

- [Acoustic and Machine Learning Methods for Speech-Based Suicide Risk Assessment: A Systematic Review](https://mlsanity.com/paper/2505.18195/) (2025)
- [Suicide Risk Assessment Using Multimodal Speech Features: A Study on the SW1 Challenge Dataset](https://mlsanity.com/paper/2505.13069/) (2025)
- [Language-Agnostic Suicidal Risk Detection Using Large Language Models](https://mlsanity.com/paper/2505.20109/) (2025)
- [Leveraging Large Language Models for Spontaneous Speech-Based Suicide Risk Detection](https://mlsanity.com/paper/2507.00693/) (2025)
- [Dynamic Fusion Multimodal Network for SpeechWellness Detection](https://mlsanity.com/paper/2508.18057/) (2025)
- [SpeechDx: A Multi-Task Benchmark for Clinical Speech AI](https://mlsanity.com/paper/2606.17339/) (2026)

## Abstract (arXiv)

The 1st SpeechWellness Challenge (SW1) aims to advance methods for detecting current suicide risk in adolescents using speech analysis techniques. Suicide among adolescents is a critical public health issue globally. Early detection of suicidal tendencies can lead to timely intervention and potentially save lives. Traditional methods of assessment often rely on self-reporting or clinical interviews, which may not always be accessible. The SW1 challenge addresses this gap by exploring speech as a non-invasive and readily available indicator of mental health. We release the SW1 dataset which contains speech recordings from 600 adolescents aged 10-18 years. By focusing on speech generated from natural tasks, the challenge seeks to uncover patterns and markers that correlate with current suicide risk.