# Non-Invasive Suicide Risk Prediction Through Speech Analysis

arXiv: https://arxiv.org/abs/2404.12132 · HTML: https://mlsanity.com/paper/2404.12132/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 18 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [Dawn of the transformer era in speech emotion recognition: closing the valence gap](https://mlsanity.com/paper/2203.07378/) (2022)

## Built on by (most cited first, 3 of 3)

- [Deep Learning and Large Language Models for Audio and Text Analysis in Predicting Suicidal Acts in Chinese Psychological Support Hotlines](https://mlsanity.com/paper/2409.06164/) (2024)
- [An Exploratory Deep Learning Approach for Predicting Subsequent Suicidal Acts in Chinese Psychological Support Hotlines](https://mlsanity.com/paper/2408.16463/) (2024)
- [Acoustic and Machine Learning Methods for Speech-Based Suicide Risk Assessment: A Systematic Review](https://mlsanity.com/paper/2505.18195/) (2025)

## Abstract (arXiv)

The delayed access to specialized psychiatric assessments and care for patients at risk of suicidal tendencies in emergency departments creates a notable gap in timely intervention, hindering the provision of adequate mental health support during critical situations. To address this, we present a non-invasive, speech-based approach for automatic suicide risk assessment. For our study, we collected a novel speech recording dataset from 20 patients. We extract three sets of features, including wav2vec, interpretable speech and acoustic features, and deep learning-based spectral representations. We proceed by conducting a binary classification to assess suicide risk in a leave-one-subject-out fashion. Our most effective speech model achieves a balanced accuracy of 66.2,%. Moreover, we show that integrating our speech model with a series of patients' metadata, such as the history of suicide attempts or access to firearms, improves the overall result. The metadata integration yields a balanced accuracy of 94.4,%, marking an absolute improvement of 28.2,%, demonstrating the efficacy of our proposed approaches for automatic suicide risk assessment in emergency medicine.