Read speech voice quality and disfluency in individuals with recent suicidal ideation or suicide attempt.

Individuals that have incurred trauma due to a suicide attempt often acquire residual health complications, such as cognitive, mood, and speech-language disorders. Due to limited access to suicidal speech audio corpora, behavioral differences in patients with a history of suicidal ideation and/or behavior have not been thoroughly examined using subjective voice quality and manual disfluency measures. In this study, we examine the Butler-Brown Read Speech (BBRS) database that includes 20 healthy controls with no history of suicidal ideation or behavior (HC group) and 226 psychiatric inpatients with recent suicidal ideation (SI group) or a recent suicide attempt (SA group). During read aloud sentence tasks, SI and SA groups reveal poorer average subjective voice quality composite ratings when compared with individuals in the HC group. In particular, the SI and SA groups exhibit average 'grade' and 'roughness' voice quality scores four to six times higher than those of the HC group. We demonstrate that manually annotated voice quality measures, converted into a low-dimensional feature vector, help to identify individuals with recent suicidal ideation and behavior from a healthy population, generating an automatic classification accuracy of up to 73%. Furthermore, our novel investigation of manual speech disfluencies (e.g., manually detected hesitations, word/phrase repeats, malapropisms, speech errors, non-self-correction) shows that inpatients in the SI and SA groups produce on average approximately twice as many hesitations and four times as many speech errors when compared with individuals in the HC group. We demonstrate automatic classification of inpatients with a suicide history from individuals with no suicide history with up to 80% accuracy using manually annotated speech disfluency features. Knowledge regarding voice quality and speech disfluency behaviors in individuals with a suicide history presented herein will lead to a better understanding of this complex phenomenon and thus contribute to the future development of new automatic speech-based suicide-risk identification systems.