Significance: We addressed the question, “How do corticobasal ganglia projecting neurons contribute to vocal learning?” We performed specific ablation of the vocal cortical neurons projecting to the basal ganglia, HVC _(X) neurons in a songbird, which generate temporally precise firing during singing. Specific ablation of HVC _(X) neurons in juveniles caused deficits in learning the tutor song’s acoustics and less consistency of song sequence. In contrast, adult HVC _(X) neuron ablation did not affect the degree of vocal fluctuations or cause alteration in song structure by auditory feedback inhibition. These results support the hypothesis that HVC _(X) neurons are a neural substrate for transferring temporal signals, but not for regulating vocal fluctuations or conveying auditory feedback, to the basal ganglia for vocal learning and maintenance.

In general, it is common knowledge that people’s feelings are reflected in their voice and facial expressions. This research work focuses on developing techniques for diagnosing depression based on acoustic properties of the voice. In this study, we developed a composite index of vocal acoustic properties that can be used for depression detection. Voice recordings were collected from patients undergoing outpatient treatment for major depressive disorder at a hospital or clinic following a physician’s diagnosis. Numerous features were extracted from the collected audio data using openSMILE software. Furthermore, qualitatively similar features were combined using principal component analysis. The resulting components were incorporated as parameters in a logistic regression based classifier, which achieved a diagnostic accuracy of ~90% on the training set and ~80% on the test set. Lastly, the proposed metric could serve as a new measure for evaluation of major depressive disorder.

Voice-based depression detection methods have been studied worldwide as an objective and easy method to detect depression. Conventional studies estimate the presence or severity of depression. However, an estimation of symptoms is a necessary technique not only to treat depression, but also to relieve patients’ distress. Hence, we studied a method for clustering symptoms from HAM-D scores of depressed patients and by estimating patients in different symptom groups based on acoustic features of their speech. We could separate different symptom groups with an accuracy of 79%. The results suggest that voice from speech can estimate the symptoms associated with depression.