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	Decision tree based state tying for speech recognition using DNN derived embeddings
	Li, Xiangang ; Wu, Xihong
	2014
关键词	decision tree based state tying DNN embedding speech recognition clustering NEURAL-NETWORKS
英文摘要	Recently, context dependent (CD)-deep neural network (DNN)-hidden Markov model (HMM) obtains significant improvements in many automatic speech recognition (ASR) tasks. In the standard training procedure for CD-DNN-HMM, the Gaussian mixture models (GMM) based ASR system has to be firstly built to pre-segment the training data and to define the CD states as the targets for DNN. In this paper, we propose a novel decision tree based state tying procedure, in which, the state embeddings derived from DNN are used and clustered to minimize the sum-of-squared error. Thus, the GMM is not a necessary part to define the targets for CD-DNN. Besides, we introduce a training procedure for CD-DNN-HMM, where, the forward backward algorithm is used for context independent (CI) DNN-HMM training, and the proposed state tying approach is applied to define the CD-DNN targets. Experiments were conducted on a 30-hour Chinese broadcast news speech database and the results demonstrate that the proposed DNN based state tying approach yielded comparable performance to the GMM based one.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000349765600026&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Computer Science, Artificial Intelligence; CPCI-S(ISTP); 0
语种	英语
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/292409]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Li, Xiangang,Wu, Xihong. Decision tree based state tying for speech recognition using DNN derived embeddings. 2014-01-01.

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