Automatic prediction of suicidal risk in military couples using multimodal interaction cues from couples conversations

TL;DR

This study explores whether multimodal cues from military couples' conversations can automatically predict suicidal risk levels, aiming for early detection using speech and behavior analysis in real-world noisy environments.

Contribution

It introduces an automatic system that classifies suicidal risk from multimodal interaction cues, demonstrating effectiveness in real-world noisy conditions and highlighting the importance of behavior and turn-taking cues.

Findings

Automatic system outperforms chance in classifying suicidal risk levels.

Behavior and turn-taking cues are the most informative features.

Gender and topic conditioning improve classification accuracy.

Abstract

Suicide is a major societal challenge globally, with a wide range of risk factors, from individual health, psychological and behavioral elements to socio-economic aspects. Military personnel, in particular, are at especially high risk. Crisis resources, while helpful, are often constrained by access to clinical visits or therapist availability, especially when needed in a timely manner. There have hence been efforts on identifying whether communication patterns between couples at home can provide preliminary information about potential suicidal behaviors, prior to intervention. In this work, we investigate whether acoustic, lexical, behavior and turn-taking cues from military couples' conversations can provide meaningful markers of suicidal risk. We test their effectiveness in real-world noisy conditions by extracting these cues through an automatic diarization and speech recognition front-end. Evaluation is performed by classifying 3 degrees of suicidal risk: none, ideation, attempt. Our automatic system performs significantly better than chance in all classification scenarios and we find that behavior and turn-taking cues are the most informative ones. We also observe that conditioning on factors such as speaker gender and topic of discussion tends to improve classification performance.