Attribute Knowledge Integration for Speech Recognition Based on Multi-task Learning Neural Networks

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	Attribute Knowledge Integration for Speech Recognition Based on Multi-task Learning Neural Networks
	Hao Zheng1 ; Zhanlei Yang1 ; Liwei Qiao 2; Jianping Li 2; Wenju Liu1
	2015
会议日期	2015
会议地点	Dresden, Germany
关键词	Multi-task Learning Automatic Attribute Transcription Deep Neural Networks
英文摘要	It has been demonstrated that the speech recognition performance can be improved by adding extra articulatory information, and subsequently, how to use such information effectively becomes a challenging problem. In this paper, we propose an attribute-based knowledge integration architecture which is realized by modeling and learning both acoustic and articulatory cues simultaneously in a uniform framework. The framework promotes the performance by providing attribute-based knowledge in both feature and model domains. In model domain, the attribute classification is used as the secondary task to improve the performance of an MTL-DNN used for speech recognition by lifting the discriminative ability on pronunciation. In feature domain, an attribute-based feature is extracted from an MTL-DNN trained with attribute classification as its primary task and phonetic/tri-phone state classification as the secondary task. Experiments on TIMIT and WSJ corpuses show that the proposed framework achieves significant performance improvements compared with the baseline DNN-HMM systems.
会议录	INTERSPEECH
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/11779]
专题	自动化研究所_模式识别国家重点实验室_机器人视觉团队
通讯作者	Hao Zheng
作者单位	1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.Electric Power Research Institute of Shanxi Electric Power Company
推荐引用方式 GB/T 7714	Hao Zheng,Zhanlei Yang,Liwei Qiao,et al. Attribute Knowledge Integration for Speech Recognition Based on Multi-task Learning Neural Networks[C]. 见:. Dresden, Germany. 2015.