Schizophrenia identification for phonetic coherence using SVM and blur approaches

Article type: Research Article

Authors: Xu, Huiyan^{; *}

Affiliations: School of Information and Mechanical Engineering, Hunan International Economics University, Changsha, China

Correspondence: [*] .Corresponding author. Huiyan Xu, School of Information and Mechanical Engineering, Hunan International Economics University, Changsha, China. E-mail: [email protected].

Abstract: The diagnosis cycle of schizophrenia is long, there is no objective diagnostic basis. The over-energy entropy product of the speech fluency rectangular parameter is designed in the paper, the fuzzy clustering is used to double locate speech pause areas and to assist in the diagnosis of schizophrenia. The pause area of speech is located based on the low speech fluency and flat energy in schizophrenia patients, an extraction algorithm is given for speech fluency quantification parameters, support vector machine (SVM) classifier is used in the approach. The fluency acoustic features of speech are taken from 28 schizophrenia patients and 28 normal controls, these are used to verify the effect of the method in schizophrenia recognition, there is a correct rate of over 85%. The automatic schizophrenia identification based on energy entropy product and fuzzy clustering can provide objective, effective and non-invasive auxiliary for clinical diagnosis of schizophrenia.

Keywords: Schizophrenia, speech fluency rectangle parameter, fuzzy clustering, hyperenergy entropy product, speech pauses in schizophrenia

DOI: 10.3233/JIFS-220248

Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3137-3151, 2023