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	CONSTRUCTING LONG SHORT-TERM MEMORY BASED DEEP RECURRENT NEURAL NETWORKS FOR LARGE VOCABULARY SPEECH RECOGNITION
	Li, Xiangang ; Wu, Xihong
	2015
关键词	long short-term memory recurrent neural networks deep neural networks acoustic modeling large vocabulary speech recognition
英文摘要	Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks (RNNs), alternative deep LSTM architectures are proposed and empirically evaluated on a large vocabulary conversational telephone speech recognition task. Meanwhile, regarding to multi-GPU devices, the training process for LSTM networks is introduced and discussed. Experimental results demonstrate that the deep LSTM networks benefit from the depth and yield the state-of-the-art performance on this task.; CPCI-S(ISTP); lixg@cis.pku.edu.on; wxh@cis.pku.edu.on; 4520-4524
语种	英语
出处	40th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/450368]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Li, Xiangang,Wu, Xihong. CONSTRUCTING LONG SHORT-TERM MEMORY BASED DEEP RECURRENT NEURAL NETWORKS FOR LARGE VOCABULARY SPEECH RECOGNITION. 2015-01-01.

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