  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.
