OBJECTIVES: 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. RESULTS AND 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.
OBJECTIVES: 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 depressedpatients, and 10 non-depressed control subjects. Gaussian mixtures were employed to model the class distributions of the extracted features. RESULTS AND 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 depressedpatients and non-depressed controls, and 80% between near-term suicidal patients and depressedpatients.