  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.
