Exploring Gender-Specific Speech Patterns in Automatic Suicide Risk Assessment
This addresses timely intervention for suicide risk in emergency medicine, though it is incremental with a small dataset.
The paper tackles automatic suicide risk assessment from speech by introducing a novel dataset of 20 patients and exploring gender-specific modeling, achieving 81% balanced accuracy in discriminating high- from low-risk cases using emotion fine-tuned wav2vec2.0 features.
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.