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	Integrating prosodic information into recurrent neural network language model for speech recognition
	Fu, Tong ; Han, Yang ; Li, Xiangang ; Liu, Yi ; Wu, Xihong
	2015
英文摘要	Prosody is a kind of cues that are critical to human speech perception and comprehension, so it is plausible to integrate prosodic information into machine speech recognition. However, as a result of the supra-segmental nature, it is hard to integrate prosodic information with conventional acoustic features. Recently, RNNLMs have shown to be the state-of-the-art language model in many tasks. We thus attempt to integrate prosodic information into RNNLMs for improving speech recognition performance based on rescoring strategy. Firstly, three word-level prosodic features are extracted from speech and then passed to RNNLMs separately. Therefore RNNLMs predict the next word based on prosodic features and word history. Experiments conducted on LibriSpeech Corpus show that the word error rate decreases from 8.07% to 7.96%. Secondly, prosodic information is combined on feature-level and model-level for further improvements and word error rate decreases 4.71% relatively. ? 2015 Asia-Pacific Signal and Information Processing Association.; EI; 1194-1197
语种	英语
出处	2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
DOI标识	10.1109/APSIPA.2015.7415462
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/449526]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Fu, Tong,Han, Yang,Li, Xiangang,et al. Integrating prosodic information into recurrent neural network language model for speech recognition. 2015-01-01.

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