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.
Bibliographical noteFunding Information:
This work was funded by the National Natural Science Foundation of China (Grant Number: 61872038).
© 2022, The Author(s), under exclusive licence to Springer Nature B.V.
- Machine learning
- Speech analysis
- Suicide detection
- Suicide ideation detection
- Visual cues