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A Modified HME Architecture for Text-Dependent Speaker Identification

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ABSTRACT
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 talkers. Experimental results demonstrate that the proposed architecture outperforms the original HME architecture in text-dependent speaker identification.

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Click tnn96.pdf for full text and tnn96c.pdf for the correction to Figure 1.

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