GMM versus AR-Vector Models for Text Independent Speaker Verification

Charles B. de Lima, Abraham Alcaim, José A. Apolinário Jr.

DOI: 10.14209/its.2002.817

Evento: 2002 International Telecommunications Symposium (ITS2002)

Keywords:

Abstract

"This paper presents a performance evaluation of two classification systems for text independent speaker verification: the Gaussian Mixture Model (GMM) and the AR-Vector Model. For the GMM, 32, 16, 8 and Gaussians are evaluated. On the other hand, an order 2 model with the Itakura symmetric distance was used for the AR-Vector. Both classification systems presented no errors when training and testing times were not smaller than 60s and 30s, respectively. Using s as the test time, the most accurate classification systems errors were between 0.4 and 3.3%. With 3s test, the errors presented by the GMM were around 6 to 7% whereas those for the AR-Vector were above 10%. However, the best results using 10s as testing and training times were obtained with the AR-Vector, with errors around 3.2%."

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