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Artificial intelligence for suicide assessment using Audiovisual Cues: a review


Death by suicide is the seventh leading death cause worldwide. The recent advancement in Artificial Intelligence (AI), specifically AI applications in image and voice processing, has created a promising opportunity to revolutionize suicide risk assessment. Subsequently, we have witnessed fast-growing literature of research that applies AI to extract audiovisual non-verbal cues for mental illness assessment. However, the majority of the recent works focus on depression, despite the evident difference between depression symptoms and suicidal behavior non-verbal cues. In this paper, we review the recent works that study suicide ideation and suicide behavior detection through audiovisual feature analysis, mainly suicidal voice/speech acoustic features analysis and suicidal visual cues. Automatic suicide assessment is a promising research direction that is still in the early stages. Accordingly, there is a lack of large datasets that can be used to train machine leaning and deep learning models proven to be effective in other, similar tasks.

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This work was funded by the National Natural Science Foundation of China (Grant Number: 61872038).

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Correspondence to Huansheng Ning.

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Dhelim, S., Chen, L., Ning, H. et al. Artificial intelligence for suicide assessment using Audiovisual Cues: a review. Artif Intell Rev (2022).

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  • Suicide detection
  • Machine learning
  • Speech analysis
  • Visual cues
  • Suicide ideation detection