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Suicide is a major societal challenge globally, with a wide range of riskfactors, from individual health, psychological and behavioral elements tosocio-economic aspects. Military personnel, in particular, are at especiallyhigh risk. Crisis resources, while helpful, are often constrained by access toclinical visits or therapist availability, especially when needed in a timelymanner. There have hence been efforts on identifying whether communicationpatterns between couples at home can provide preliminary information aboutpotential suicidal behaviors, prior to intervention. In this work, weinvestigate whether acoustic, lexical, behavior and turn-taking cues frommilitary couples' conversations can provide meaningful markers of suicidalrisk. We test their effectiveness in real-world noisy conditions by extractingthese 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 thanchance in all classification scenarios and we find that behavior andturn-taking cues are the most informative ones. We also observe thatconditioning on factors such as speaker gender and topic of discussion tends toimprove classification performance.

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