Speaker Recognition Based on GMM with an Embedded TDNN

Authors:
Cunbao Chen;Li Zhao
Affiliations:
School of information science and engineering, Southeast University, Nanjing, China 210096;School of information science and engineering, Southeast University, Nanjing, China 210096
Venue:
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Year:
2009

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Abstract

A modified GMM with an embedded TDNN is proposed to speaker recognition. The model integrates the merits of GMM and TDNN. TDNN is used to digest the time information of the feature sequences, and through the transformation of the feature vectors the model makes the hypothesis of variable independence which maximum likelihood needed more reasonable. In the process of training, GMM and TDNN are trained as a whole and the parameters of GMM and TDNN are updated alternately. Experiments show that the proposed model improves accuracy rate against baseline GMM at all SNR with a maximum to 22%.