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Abstract
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| MFCC coefficients extracted from the power spectral density of speech as a whole, seems to have become the de facto standard in the area of speaker recognition, as demonstrated by its use in almost all systems submitted to the 2013 Speaker Recognition Evaluation (SRE) in Mobile Environment [1], thus relegating to background this component of the recognition systems. How-ever, in this article we will show that selecting the adequate speaker characteri-zation system is as important as the selection of the classifier. To accomplish this we will compare the recognition rates achieved by different recognition systems that relies on the same classifier (GMM-UBM) but connected with dif-ferent feature extraction systems (based on both classical and biometric parame-ters). As a result we will show that a gender dependent biometric parameteriza-tion with a simple recognition system based on GMM-UBM paradigm provides very competitive or even better recognition rates when compared to more com-plex classification systems based on classical features. | |
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International
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Book Edition
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Book Publishing
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Universidad de Las Palmas de Gran Canaria |
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ISBN
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978-84-695-8101-8 |
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Series
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Book title
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Proc. I Jornadas Multidisciplinares de Usuarios de la Voz, el Habla y el Canto |
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From page
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122 |
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To page
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131 |