Title: Identification and classification of schizophrenic speech using convolutional neural network for medical healthcare

Authors: Akshita Abrol; Nisha Kapoor; Parveen Kumar Lehana

Addresses: Department of Electronics, University of Jammu, Jammu, J&K 180006, India ' School of Biotechnology, University of Jammu, Jammu, J&K 180006, India ' Department of Electronics, University of Jammu, Jammu, J&K 180006, India

Abstract: Schizophrenia is a brain disorder that significantly affects the quality of life of affected individuals. One of its prominent symptoms is the induction of changes in the acoustics of the patients. In the absence of definite methods for its diagnosis, speech analysis can help in the preliminary screening of the patients. In this paper, an automated method using deep learning for differentiating between individuals with schizophrenia and psychosis from healthy individuals is suggested. Using convolutional neural networks with speech spectrograms as input, a classification accuracy of 87.01% has been obtained for levels of schizophrenia and 95.26% for differentiating between schizophrenic and healthy speech.

Keywords: schizophrenia; convolutional neural network; CNN; deep learning; spectrograms.

DOI: 10.1504/IJMEI.2023.134534

International Journal of Medical Engineering and Informatics, 2023 Vol.15 No.6, pp.540 - 548

Received: 03 May 2021
Accepted: 03 Aug 2021
Published online: 27 Oct 2023 *

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