Speaker verification using Gaussian mixture model (GMM)

Abstract

This paper applies GMM for SV on Malay speech. The speaker models were trained on maximum likelihood estimated. The system was evaluated with 23 client speakers with 56 imposters. Malay clean speech data was used. 20 training of 3.5s utterances are used. The best performance achieved using 256-Gaussian imposter model and 32-Gaussian client model gave 3.01% of EER. © 2011 Springer-Verlag.