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| Speaker Identification Based on Classify Feature Sub-space Gaussian Mixture Model and Neural Net Fusion |
| Huang Wei; Dai Bei-qian; Li Hui |
| Electron. Sci. and Tech. Dept Univ. of Sci. and Tech. of China Hefei 230026 China |
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Abstract In this paper, a speaker identification system is proposed based on classify Fea-ture Sub-space Gaussian Mixture Model and Neural Net fusion (FS-GMM/NN) . With clus-tering analysis of the feature vectors, the speaker’s training feature vectors can be classified to some subsets and training classify Gaussian Mixture Models (GMM) with different mix-tures according to the subset’s feature vectors’s number. Finally, the outputs of every classify GMM will be fused by Neural Net (NN). In the experiment of text-independent speaker iden-tification of 100 speakers (male), the system based on FS-GMM/NN overmatch the Baseline Gaussian Mixture Model (B-GMM) in identification performance and noise robustness with fewer mixtures and shorter test speech. Moreover, the training of FS-GMM/NN is more effective.
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Received: 16 May 2003
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2004, Vol. 26 
