Distinguishing depression and suicidal risk in men using GMM based frequency contents of affective vocal tract response

Two types of speech recording collected from three groups of male subjects clinically diagnosed with depression, remission from depression, and suicidal potential were analyzed and investigated for their acoustic features derived from sub-band energy over 0-2 KHz and GMM-based spectrum of the vocal tract response. Spontaneous and text-reading speech samples characterized by different vocal features revealed significant between-class separation power. Especially, features extracted from the reading speech seemed to provide more separability between classes than those of the spontaneous speech. Additionally, high classification accuracy confirmed that the studied features were capable of distinguishing groups of different diagnostic subjects efficiently. In classifying depressed/suicidal subjects the correct score of classification was at 88.5% for features extracted from reading speech samples, while 85.58% was found from classifying spontaneous speech features. These results were considered to be fairly high in classification performance, which is supportive of the promising ability to distinguish two diagnostic groups whose speech samples changed in their acoustic properties and correlated of serious mental states, known as vocal affects. Our findings suggested some clues in diagnosis of psychiatric disorders for psychiatrist.