標題: | DEEP RECURRENT REGULARIZATION NEURAL NETWORK FOR SPEECH RECOGNITION |
作者: | Chien, Jen-Tzung Lu, Tsai-Wei 電機工程學系 Department of Electrical and Computer Engineering |
關鍵字: | Recurrent neural network;model regularization;deep learning;acoustic model |
公開日期: | 1-一月-2015 |
摘要: | This paper presents a deep recurrent regularization neural network (DRRNN) for speech recognition. Our idea is to build a regularization neural network acoustic model by conducting the hybrid Tikhonov and weight-decay regularization which compensates the variations due to the input speech as well as the model parameters in the restricted Boltzmann machine as a pre-training stage for feature learning and structural modeling. In addition, a new backpropagation through time (BPTT) algorithm is developed by extending the truncated minibatch training for recurrent neural network where the minibatch BPTT is not only performed in recurrent layer but also in feedforward layer. The DRRNN acoustic model is accordingly established to capture the temporal correlation in a regularization neural network. Experimental results on the tasks of RM and Aurora4 show the effectiveness and robustness of using DRRNN for speech recognition. |
URI: | http://hdl.handle.net/11536/150695 |
ISSN: | 1520-6149 |
期刊: | 2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP) |
起始頁: | 4560 |
結束頁: | 4564 |
顯示於類別: | 會議論文 |