Artificial intelligence for suicide assessment using Audiovisual Cues: a review

Sahraoui Dhelim, Luke Chen, Huansheng Ning, CD Nugent

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
23 Downloads (Pure)

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 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.
Original languageEnglish
Pages (from-to)1-29
Number of pages29
JournalArtificial Intelligence Review
Early online date2 Nov 2022
DOIs
Publication statusPublished online - 2 Nov 2022

Bibliographical note

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

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature B.V.

Keywords

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

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