title: Analysis of vocal tract characteristics for near-term suicidal risk assessment.
abstract: OBJECTIVE
  Among the many clinical decisions that psychiatrists must make, assessment of a patient's risk of committing suicide is definitely among the most important, complex and demanding. One of the authors reviewing his clinical experience observed that successful predictions of suicidality were often based on the patient's voice independent of content. The voices of suicidal patients exhibited unique qualities, which distinguished them from non-suicidal patients. In this study we investigated the discriminating power of lower order mel-cepstral coefficients among suicidal, major depressed, and non-suicidal patients.
  
  
  METHODS
  Our sample consisted of 10 near-term suicidal patients, 10 major depressed patients, and 10 non-depressed control subjects. Gaussian mixtures were employed to model the class distributions of the extracted features.
  
  
  CONCLUSIONS
  As a result of two-sample ML classification analyses, first four mel-cepstral coefficients yielded exceptional classification performance with correct classification scores of 80% between near-term suicidal patients and non-depressed controls, 75% between depressed patients and non-depressed controls, and 80% between near-term suicidal patients and depressed patients.
authors.affiliation: Vanderbilt University, Biomedical Engineering, Box 1554B, Nashville, TN 37235, USA. richard.shiavi@vanderbilt.edu
authors.forename: A
authors.initials: A
authors.lastname: Ozdas
authors.forename: R G
authors.initials: RG
authors.lastname: Shiavi
authors.forename: D M
authors.initials: DM
authors.lastname: Wilkes
authors.forename: M K
authors.initials: MK
authors.lastname: Silverman
authors.forename: S E
authors.initials: SE
authors.lastname: Silverman
mesh_terms: D001698:Biomedical Engineering; D003863:Depression / Q000503:physiopathology / Q000517:prevention & control / Q000523:psychology; D006801:Humans; D008297:Male; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012815:Signal Processing, Computer-Assisted; D013061:Speech Acoustics; D013405:Suicide / Q000523:psychology*; D014833:Voice Quality / Q000502:physiology*; D000092864:Suicide Prevention