Feature based classification of dysfluent and normal speech

  • Authors:

  • P. Mahesha;D. S. Vinod

  • Affiliations:

  • S. J. College of Engineering, Mysore;S. J. College of Engineering, Mysore

  • Venue:
  • Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper is intended to develop a new approach for identification and classification of dysfluent and normal speech using Mel-Frequency Cepstral Coefficient (MFCC). Stuttering is a fluency disorder in which the fluency of speech is interrupted by occurrences of dysfluencies such as repetitions, prolongations, interjections, silent pauses, broken words, incomplete phrases and revisions. In this work we have considered three types of dysfluencies such as repetition, prolongation and interjection to characterize stuttered speech. After obtaining dysfluent and normal speech, the speech signals are analyzed in order to extract MFCC features. The k-Nearest Neighbor(k-NN) classifier is used to classify the speech as dysfluent and normal speech. The 80% of the data is used for training and 20% for testing. The avarage accuracy of 86.67% and 93.34% is obtained for dysfluent and normal speech respectively.