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A modified HME architecture for text-dependent speaker identification

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3 Author(s)
Ke Chen ; Nat. Lab. of Machine Perception, Beijing Univ., China ; Dahong Xie ; Huisheng Chi

A modified hierarchical mixtures of experts (HME) architecture is presented for text-dependent speaker identification. A new gating network is introduced to the original HME architecture for the use of instantaneous and transitional spectral information in text-dependent speaker identification. The statistical model underlying the proposed architecture is presented and learning is treated as a maximum likelihood problem; in particular, an expectation-maximization (EM) algorithm is also proposed for adjusting the parameters of the proposed architecture. An evaluation has been carried out using a database of isolated digit utterances by 10 male speakers. Experimental results demonstrate that the proposed architecture outperforms the original HME architecture in text-dependent speaker identification

Published in:

IEEE Transactions on Neural Networks  (Volume:7 ,  Issue: 5 )