CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Improving Large Vocabulary Accented Mandarin Speech Recognition with Attribute-based I-vectors
Hao Zheng1; Shanshan Zhang1; Liwei Qiao2; Jianping Li2; Wenju Liu1
2016
会议名称INTERSPEECH
会议录名称INTERSPEECH
会议日期2016
会议地点San Francisco, America
摘要It has been well-recognized that the accent has a great impact on the ASR of Chinese Mandarin, therefore, how to improve the performance on the accented speech has become a critical issue in this field. The attribute feature has been proven effective
on modelling accented speech, resulting in a significantly improved performance in accent recognition. In this paper, we propose an attribute-based i-vector to improve the performance of speech recognition system on large vocabulary accented Mandarine speech task. The system with proposed attribute features works well especially with sufficient training data. To further promote the performance on conditions such as resource limited condition or training data mismatched condition, we also develop Multi-Task Learning Deep Neural Networks (MTL-DNNs) with attribute classification as the secondary task to improve the discriminative ability on Mandarin speech. Experiments
on the 450-hour Intel accented Mandarin speech corpus demonstrate that the system with attribute-based i-vectors achieves a significant performance improvement on sufficient training data compared with the baseline DNN-HMM system. The MTL-DNNs complement the shortage of attribute-based ivectors on data limited and mismatched conditions and obtain obvious CER reductions.
关键词Accented Speech Recognition Large Vocabulary Continuous Speech Recognition Attribute
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11781
专题模式识别国家重点实验室_机器人视觉
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,Shanshan Zhang,Liwei Qiao,et al. Improving Large Vocabulary Accented Mandarin Speech Recognition with Attribute-based I-vectors[C],2016.
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