Enhancing Suicide Risk Assessment: A Speech-Based Automated Approach in
  Emergency Medicine
Shahin Amiriparian Maurice Gerczuk Justina Lutz Wolfgang Strube Irina Papazova Alkomiet Hasan Alexander Kathan Björn W. Schuller
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 have collected a novel dataset of speech recordings from $20$
patients from which 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.