Non-Invasive Suicide Risk Prediction Through Speech Analysis
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916460258918400 |
|---|---|
| author | Amiriparian, Shahin Gerczuk, Maurice Lutz, Justina Strube, Wolfgang Papazova, Irina Hasan, Alkomiet Kathan, Alexander Schuller, Björn W. |
| author_facet | Amiriparian, Shahin Gerczuk, Maurice Lutz, Justina Strube, Wolfgang Papazova, Irina Hasan, Alkomiet Kathan, Alexander Schuller, Björn W. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_12132 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Non-Invasive Suicide Risk Prediction Through Speech Analysis Amiriparian, Shahin Gerczuk, Maurice Lutz, Justina Strube, Wolfgang Papazova, Irina Hasan, Alkomiet Kathan, Alexander Schuller, Björn W. Sound Computation and Language Audio and Speech Processing I.2 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. |
| title | Non-Invasive Suicide Risk Prediction Through Speech Analysis |
| topic | Sound Computation and Language Audio and Speech Processing I.2 |
| url | https://arxiv.org/abs/2404.12132 |