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.
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