Artificial Intelligence for Suicide Assessment using Audiovisual Cues: A Review

TL;DR

This review discusses how AI analyzing audiovisual cues can aid in suicide risk assessment, highlighting recent research, challenges, and the early stage of this promising but underdeveloped field.

Contribution

It provides a comprehensive overview of AI-based audiovisual methods for suicide detection, emphasizing the need for larger datasets and further research.

Findings

AI shows potential in suicide risk assessment through audiovisual cues

Most studies focus on speech and visual cues related to suicidal behavior

The field is in early development with limited datasets

Abstract

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 and non-verbal cues. This paper reviews 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 learning and deep learning models proven to be effective in other, similar tasks.