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Classification of Pathological Voices Using Glottal Signal Parameters

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The discrimination of voice signals has numerous applications in diagnosing of pathologies related to voice. This paper discussed about the glottal signal that is bound to recognize two sorts of voice issue: Laryngitis and Laryngeal dystonia (LD). The parameters of the glottal signal fill in as contribution to classifiers that characterizes into three unique gatherings of speakers: speakers with Laryngitis; with laryngeal dystonia (LD); lastly speakers with healthy voices. The database is made out of voice accounts containing tests of three gatherings. The classifiers SVM provided 60%, KNN provided 70% and Ensemble provided 80% classification accuracy in the case of Laryngitis. Voice signals of patients affected with Laryngeal dystonia were also collected and tested with same classifiers and the Accuracy of 90%, 80% and 50% were obtained with SVM, KNN and Ensemble respectively.
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Keywords: Ensemble; Glottal Signal Parameters; Laryngeal Dystonia (LD); Laryngitis; Vocal Cord Diseases

Document Type: Research Article

Affiliations: 1: Ph.D. Scholar, School of VLSI Design and Embedded Systems, National Institute of Technology, Kurukshetra 136119, India 2: Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra 136119, India

Publication date: September 1, 2019

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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