Investigating the speech characteristics of suicidal adolescents

Suicide is a very serious problem. In the United states it ranks as the second most frequent cause of death among teenagers between the ages of 12 and 17. In this work, we investigate speech characteristics of prosody as well as voice quality in a dyadic interview corpus with suicidal and non-suicidal adolescents. In these interviews the adolescents answer specifically designed questions. Based on this limited dataset, we reveal statistically significant differences in the speech patterns of suicidal adolescents within the investigated interview corpus. Further, we investigate the classification capabilities of machine learning approaches both on an utterance as well as an interview level. The work shows promising results in a speaker-independent classification experiment based on only a dozen speech features. We believe that once the algorithms are refined and integrated with other methods, they may be of value to the clinician.