Purpose Psychiatry still needs objective biomarkers. In the context of schizophrenia, there are speech abnormalities such as tangentiality, derailment, alogia, neologisms, poverty of speech, and aprosodia. There is a growing interest in speech signals features as possible indicators of schizophrenia. This article aims to develop an intelligent tool for detection of schizophrenia using vocal patterns and machine learning techniques. The main advantages of this type of solution are the low cost, high performance, and for being non-invasive. Methods Thirty-one individuals over 18 years old were selected, 20 with previous diagnosis of schizophrenia, and 11 healthy controls. Their speech was audio-recorded in naturalistic settings, during a routine medical assessment for psychiatric patients. In the case of healthy patients, the recordings were made in different environments. Recordings were pre-processed, excluding non-participant voices. We extracted 33 features. We used the particle swarm optimization algorithm for feature selection. Results The classifiers' performance was analyzed with four metrics: accuracy, sensibility, specificity, and kappa index. Best results were achieved when considering all 33 extracted features. Within machine models, support vector machines (SVM) models provided the greatest classification performance, with mean accuracy of 91.76% for PUK kernel. Our results outperform those from most studies published so far for the detection of schizophrenia based on acoustic patterns. Conclusion The use of machine learning classifiers using vocal parameters, in particular SVM, has shown to be very promising for the detection of schizophrenia. Nevertheless, further experiments with a larger sample will be necessary to validate our findings.

Purpose Diagnosis and treatment in psychiatry are still highly dependent on reports from patients and on clinician judgment. This fact makes them prone to memory and subjectivity biases. As for other medical fields, where objective biomarkers are available, there has been an increasing interest in the development of such tools in psychiatry. To this end, vocal acoustic parameters have been recently studied as possible objective biomarkers, instead of otherwise invasive and costly methods. Patients suffering from different mental disorders, such as major depressive disorder (MDD), may present with alterations of speech. These can be described as uninteresting, monotonous, and spiritless speech and low voice. Methods Thirty-three individuals (11 males) over 18 years old were selected, 22 of which being previously diagnosed with MDD and 11 healthy controls. Their speech was recorded in naturalistic settings, during a routine medical evaluation for psychiatric patients, and in different environments for healthy controls. Voices from third parties were removed. The recordings were submitted to a vocal feature extraction algorithm, and to different machine learning classification techniques. Results The results showed that random tree models with 100 trees provided the greatest classification performances. It achieved mean accuracy of 87.5575% ± 1.9490, mean kappa index, sensitivity, and specificity of 0.7508 ± 0.0319, 0.9149 ± 0.0204, and 0.8354 ± 0.0254, respectively, for the detection of MDD. Conclusion The use of machine learning classifiers with vocal acoustic features appears to be very promising for the detection of major depressive disorder in this exploratory study, but further experiments with a larger sample will be necessary to validate our findings.

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