Multilingual Recurrent Neural Networks with Residual Learning for Low-Resource Speech Recognition
Shiyu Zhou1,2; Yuanyuan Zhao1,2; Shuang Xu1; Bo Xu1
2017
会议名称Interspeech
会议录名称Interspeech
会议日期2017
会议地点Stockholm
摘要The shared-hidden-layer multilingual deep neural network (SHL-MDNN), in which the hidden layers of feed-forward deep neural network (DNN) are shared across multiple languages while the softmax layers are language dependent, has been shown to be effective on acoustic modeling of multilingual low-resource speech recognition. In this paper, we propose that the shared-hidden-layer with Long Short-Term Memory (LSTM) recurrent neural networks can achieve further performance improvement considering LSTM has outperformed DNN as the acoustic model of automatic speech recognition (ASR). Moreover, we reveal that shared-hidden-layer multilingual LSTM (SHL-MLSTM) with residual learning can yield additional moderate but consistent gain from multilingual tasks given the fact that residual learning can allievate the degradation problem of deep LSTMs. Experimental results demonstrate that SHL-MLSTM can relatively reduce word error rate (WER) by 2.1-6.8\% over SHL-MDNN trained using six languages and 2.6-7.3\% over monolingual LSTM trained using the language specific data on CALLHOME datasets. Additional WER reduction, about relatively 2\% over SHL-MLSTM, can be obtained through residual learning on CALLHOME datasets, which demonstrates residual learning is useful for SHL-MLSTM on multilingual low-resource ASR.
关键词Lstm Multilingual Speech Recognition Low-resource Residual Learning Shared-hidden-layer
收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/15421
专题数字内容技术与服务研究中心_听觉模型与认知计算
通讯作者Shiyu Zhou
作者单位1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Shiyu Zhou,Yuanyuan Zhao,Shuang Xu,et al. Multilingual Recurrent Neural Networks with Residual Learning for Low-Resource Speech Recognition[C],2017.
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