Speaker Recognition System Based on Wavelet Features and Gaussian Mixture Models
K. Sajeer1, Paul Rodrigues2
1K. Sajeer, Research Scholar, Department of Computer Science, Research and Development Centre, Bharathiar University, Coimbatore-641 046, Tamil Nadu, India.
2Paul Rodrigues, DMI College of Engineering, Palanchur, Chennai, Tamil Nadu, India.
Manuscript received on September 13, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 5363-5367 | Volume-9 Issue-1, October 2019 | Retrieval Number: A3069109119/2019©BEIESP | DOI: 10.35940/ijeat.A3069.109119
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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: Identification of a person’s voice from the different voices is known as speaker recognition. The speech signals of individuals are selected by means of speaker recognition or identification. In this work, an efficient method for speaker recognition is made by using Discrete Wavelet Transform (DWT) features and Gaussian Mixture Models (GMM) for classification is presented. The input speech signal features are decomposed by DWT into subband coefficients. The DWT subband coefficient features are the input for the classification. Classification is made by GMM classifier at 4, 8, 16 and 32 Gaussian component levels. Results show a better accuracy of 96.18% speaker signals using DWT features and GMM classifier.
Keywords: Speaker recognition, DWT transform, subband coefficient features, GMM classifier.