Attribute Knowledge Integration for Speech Recognition Based on Multi-task Learning Neural Networks
文献类型：会议论文
| 作者 | Hao Zheng1 ; Zhanlei Yang1; Liwei Qiao2; Jianping Li2; Wenju Liu1
|
| 出版日期 | 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
 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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