A Framework for Classification of Vocal Disorders without Clinical Intervention

Authors

  • Arpitha MS Dept. of Computer Science, PES College of Engineering, Mandya, Karnataka, India
  • Nagarathna N Dept. of Computer Science, PES College of Engineering, Mandya, Karnataka, India

DOI:

https://doi.org/10.26438/ijcse/v8i1.7073

Keywords:

Voice disorders,, Machine Learning, Classification, SVM, MFCC

Abstract

Voice disorders are abnormal characteristic of sound produced by larynx involving pitch, intensity, loudness. Nowadays Voice disorders are one among rapidly spreading diseases. Disordered quality of voice could also be a symptom for laryngeal diseases. The goal of this work is to build a model to identify the types of voice disorders that includes Normal, Dysphonia, Stammering and Vocal palsy. To deal with this classification problem, Machine learning classifier Support Vector Machine (SVM) is used. The results are evaluated in terms of accuracy, sensitivity, specificity and ROC based on the features extracted using Mel Frequency Cepstral Coefficients (MFCCs), they are the cepstral representation of audio clip.

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Published

2020-01-31
CITATION
DOI: 10.26438/ijcse/v8i1.7073
Published: 2020-01-31

How to Cite

[1]
M. Arpitha and N. N, “A Framework for Classification of Vocal Disorders without Clinical Intervention”, Int. J. Comp. Sci. Eng., vol. 8, no. 1, pp. 70–73, Jan. 2020.

Issue

Vol. 8 No. 1 (2020): IJCSE January Edition

Section

Research Article

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