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.
黄伟; 戴蓓蒨; 李辉. 基于分类特征空间高斯混合模型和神经网络融合的说话人识别[J]. 电子与信息学报, 2004, 26(10): 1607-1612 .
Huang Wei; Dai Bei-qian; Li Hui. Speaker Identification Based on Classify Feature Sub-space Gaussian Mixture Model and Neural Net Fusion. , 2004, 26(10): 1607-1612 .
2004, Vol. 26 
