Long short-term memory with attention and multitask learning for distant speech recognition
ZHANG Yu1,2, ZHANG Pengyuan1,2, YAN Yonghong1,2,3
1. Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China; 3. Xinjiang Laboratory of Minority Speech and Language Information Processing, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumchi 830011, China
Abstract:Distant speech recognition remains a challenging task owning to background noise, reverberation, and competing acoustic sources. This work describes a long short-term memory (LSTM) based acoustic model with an attention mechanism and a multitask learning architecture for distant speech recognition. The attention mechanism is embedded in the acoustic model to automatically tune its attention to the spliced context input which significantly improves the ability to model distant speech. A multitask learning architecture, which is trained to predict the acoustic model states and the clean features, is used to further improve the robustness. Evaluations of the model on the AMI meeting corpus show that the model reduces word error rate (WER) by 1.5% over the baseline model.
张宇, 张鹏远, 颜永红. 基于注意力LSTM和多任务学习的远场语音识别[J]. 清华大学学报(自然科学版), 2018, 58(3): 249-253.
ZHANG Yu, ZHANG Pengyuan, YAN Yonghong. Long short-term memory with attention and multitask learning for distant speech recognition. Journal of Tsinghua University(Science and Technology), 2018, 58(3): 249-253.
[1] HINTON G, DENG L, YU D, et al. Deep neural networks for acoustic modeling in speech recognition:The shared views of four research groups[J]. IEEE Signal Processing Magazine, 2012, 29(6):82-97. [2] SAK H, SENIOR A, BEAUFAYS F. Long short-term memory recurrent neural network architectures for large scale acoustic modeling[C]//15th Annual Conference of the International Speech Communication Association. Singapore:IEEE, 2014:338-342. [3] SWIETOJANSKI P, GHOSHAL A, RENALS S. Hybrid acoustic models for distant and multichannel large vocabulary speech recognition[C]//IEEE Workshop on Automatic Speech Recognition and Understanding Workshop. Olomouc, Czech Republic:IEEE, 2013:285-290. [4] BAHDANAU D, CHOROWSKI J, SERDYUK D, et al. End-to-end attention-based large vocabulary speech recognition[C]//IEEE International Conference on Acoustics, Speech and Signal Processing. Shanghai, China:IEEE, 2016:4945-4949. [5] LU L, ZHANG X, CHO K, et al. A study of the recurrent neural network encoder-decoder for large vocabulary speech recognition[C]//16th Annual Conference of the International Speech Communication Association. Dresden, Germany:IEEE, 2015:3249-3253. [6] YU D, XIONG W, DROPPO J, et al. Deep convolutional neural networks with layer-wise context expansion and attention[C]//17th Annual Conference of the International Speech Communication Association. San Francisco, CA, USA:IEEE, 2016:17-21. [7] CARLETTA J. Unleashing the killer corpus:Experiences in creating the multi-everything AMI meeting corpus[J]. Language Resources and Evaluation, 2007, 41(2):181-190. [8] BENGIO Y, SIMARD P, FRASCONI P. Learning long-term dependencies with gradient descent is diffcult[J]. IEEE Transactions on Neural Networks, 1994, 5(2):157-166. [9] PETER B, RENALS S. Regularization of context-dependent deep neural networks with context-independent multi-task training[C]//IEEE International Conference on Acoustics, Speech and Signal Processing. Brisbane, Australia:IEEE, 2015:4290-4294. [10] HUANG J T, LI J, YU D, et al. Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers[C]//IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver, Canada:IEEE, 2013:7304-7308. [11] GAO T, DU J, DAI L, et al. Joint training of front-end and back-end deep neural networks for robust speech recognition[C]//IEEE International Conference on Acoustics, Speech and Signal Processing. South Brisbane, Australia:IEEE, 2015:4375-4379. [12] POVEY D, ARNAB G, GILLES B, et al. The Kaldi speech recognition toolkit[C]//IEEE Workshop on Automatic Speech Recognition and Understanding Workshop. Hawaii, USA:IEEE, 2011.