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	Labeling Unsegmented Sequence Data with DNN-HMM and Its Application for Speech Recognition
	Li, Xiangang ; Wu, Xihong
	2014
关键词	DNN-HMM speech recognition forward-backward algorithm unsegmented sequence data HIDDEN MARKOV-MODELS NEURAL-NETWORKS
英文摘要	Recently, deep neural network (DNN) with hidden Markov model (HMM) has turned out to be a superior sequence learning framework, based on which significant improvements were achieved in many application tasks, such as automatic speech recognition (ASR). However, the training of DNN-HMM requires the pre-segmented training data, which can be generated using Gaussian Mixture Model (GMM) in ASR tasks. Thus, questions are raised by many researchers: can we train the DNN-HMM without GMM seeding, and what does it suggest if the answer is yes? In this research, we come up with the 'yes' answer by presenting forward-backward learning algorithm for DNN-HMM framework. Besides, a training procedure is proposed, in which, the training for context independent (CI) DNN-HMM is treated as the pre-training for context dependent (CD) DNN-HMM. To evaluate the contribution of this work, experiments on ASR task with the benchmark corpus TIMIT are performed, and the results demonstrate the effectiveness of this research.; 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/20.500.11897/292407]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Li, Xiangang,Wu, Xihong. Labeling Unsegmented Sequence Data with DNN-HMM and Its Application for Speech Recognition. 2014-01-01.

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