Open AccessSpeaker Recognition System Based on Wavelet Features and Gaussian Mixture Models
Author(s) -
K. Sajeer,
Paul Rodrigues
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a3069.109119
Subject(s) - pattern recognition (psychology) , discrete wavelet transform , speech recognition , mixture model , speaker recognition , artificial intelligence , computer science , classifier (uml) , speaker identification , wavelet , gaussian , wavelet transform , physics , quantum mechanics
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.