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	An HMM/MFNN hybrid architecture based on stacked generalization for speaker identification
	Bao, WQ ; Chen, K ; Chi, HS
	1998
关键词	speaker identification stacked generalization HMM/MFNN hybrid architecture
英文摘要	A hybrid architecture based upon Hidden Markov Models (HMMs) and Multilayer Feed-forward Neural Network (MFNN) is presented for speaker identification. Unlike most of the previous combing methods, the proposed architecture uses HMMs to model individual speaker and uses MFNN to deal with the inter-speaker information for improving performance. Learning in the proposed architecture consists of two phases. In particular, only a small amount of data is needed for training. The HMM/MFNN architecture has been applied to text-independent speaker identification. Simulation has shown that the hybrid architecture yields better identifying rate than that of conventional methods and other hybrid architectures.; Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Engineering, Electrical & Electronic; Medical Informatics; Neurosciences; CPCI-S(ISTP); 0
语种	英语
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/294025]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Bao, WQ,Chen, K,Chi, HS. An HMM/MFNN hybrid architecture based on stacked generalization for speaker identification. 1998-01-01.

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