Exploring Gender-Specific Speech Patterns in Automatic Suicide Risk Assessment

TL;DR

This paper presents a speech-based method for automatic suicide risk assessment, highlighting gender-specific speech patterns and achieving 81% balanced accuracy using emotion-aware deep features.

Contribution

It introduces a novel gender-specific speech analysis approach for suicide risk detection using a new dataset and deep learning features.

Findings

Gender-specific speech patterns influence suicide risk indicators.

Emotion fine-tuned wav2vec2.0 features effectively discriminate risk levels.

Discrepancies exist in speech characteristics related to suicide risk between genders.

Abstract

In emergency medicine, timely intervention for patients at risk of suicide is often hindered by delayed access to specialised psychiatric care. To bridge this gap, we introduce a speech-based approach for automatic suicide risk assessment. Our study involves a novel dataset comprising speech recordings of 20 patients who read neutral texts. We extract four speech representations encompassing interpretable and deep features. Further, we explore the impact of gender-based modelling and phrase-level normalisation. By applying gender-exclusive modelling, features extracted from an emotion fine-tuned wav2vec2.0 model can be utilised to discriminate high- from low- suicide risk with a balanced accuracy of 81%. Finally, our analysis reveals a discrepancy in the relationship of speech characteristics and suicide risk between female and male subjects. For men in our dataset, suicide risk increases together with agitation while voice characteristics of female subjects point the other way.