The Sound of the Mind: Detection of Common Mental Disorders Using Vocal Acoustic Analysis and Machine Learning
Caroline Wanderley Espinola, Juliana Carneiro Gomes, Jessiane Mônica Silva Pereira, Wellington Pinheiro dos Santos · 2022
Psychiatric diagnosises are is heavily prone to subjectivity bias from patients and clinicians. In the last few years there has been a growing effort in the development of objective markers in mental health. Vocal features appear as promising biomarkers for the detection of mental disorders and symptom severity assessment, with the advantages of scalability, cost-effectiveness, and non-invasiveness. Aims: This chapter aims Tto propose a framework for the detection of different mental disorders such as,– major depressive disorder (MDD), bipolar disorder, schizophrenia, and generalized anxiety disorder (GAD), by – using vocal acoustic analysis and machine learning models. Methods: In order to do so Wwe recorded interviews of 78 participants comprising of 66 psychiatric patients during medical visits, and 12 healthy controls. Patients belonged to four diagnostic groups, as follows: 28 patients with MDD; 20 patients with schizophrenia; 14 patients with bipolar disorder; 4 patients with GAD. Pre-processing and processing techniques were utilized for vocal features extraction. We tested the classification accuracy of several supervised machine learning models using the extracted vocal features. Results: The study found that random Forests with 300 trees achieved the greatest performance (75.27% for accuracy, 69.08% for kappa, 75.30% for sensitivity, and 93.80% for specificity) for the classification of the five categories (four disease groups and controls). The chapter ends with our finds, including the fact that Vvocal acoustic features appear to be promising biomarkers, with the advantages of being abundant, inexpensive, non-invasive and remotely performed. The results of this study are in line with previously written literature and supports the feasibility of vocal parameters screening and diagnosis in psychiatry.