Speech Recognition by Dynamic Time Warping Assisted SVM Classifier
Sanaullah Ahmad Rizvi1, M Sundararajan2
1Sanaullah Ahmad Rizvi*, Research Scholar, Department of Electronics and Communication Engineering, Bharath Institute of Higher Education and Research, Chennai, India.
2M Sundararajan, Professor, Department of Electronics and Communication Engineering, Bharath Institute of Higher Education and Research, Chennai, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on 26 November, 2019. | Manuscript published on December 10, 2019. | PP: 3879-3882 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7762129219/2019©BEIESP | DOI: 10.35940/ijitee.B7762.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Speech recognition using sustenance vector machine assisted by Dynamic time warping (DTW) method is proposed. The input training datas are collected from 40 speakers for five unique words. Every one of the information was gathered in a profoundly acoustic and commotion confirmation condition. Mel recurrence cepstrum coefficients (MFCC’s) are represented as constant property of the signal. First and second derivatives of MFCC are used for dynamic properties. Subsequent to deciding element vectors, an adjusted DTW technique is proposed for highlight coordinating. Support Vector Machine (SVM) as well as Radial basis function (RBF) are used to categorize. The model is tried for multiple speakers and a good detection rate is obtained.
Keywords: Mel frequency Cepstrum Coefficient, Delta feature, Delta-Delta feature, Dynamic time Warping, Support Vector Machine.
Scope of the Article: Pattern Recognition