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Improving robustness of deep neural network acoustic models via speech separation and joint adaptive training

机译：通过语音分离和联合自适应培训改善深神经网络声学模型的鲁棒性

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Arun Narayanan; DeLiang Wang;
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1. Improving Robustness of Deep Neural Network Acoustic Models via Speech Separation and Joint Adaptive Training [J] . Narayanan A., Wang D. Audio, Speech, and Language Processing, IEEE/ACM Transactions on . 2015,第1期

机译：通过语音分离和联合自适应训练提高深度神经网络声学模型的鲁棒性
2. A Speaker-Dependent Approach to Single-Channel Joint Speech Separation and Acoustic Modeling Based on Deep Neural Networks for Robust Recognition of Multi-Talker Speech [J] . Yan-Hui Tu, Jun Du, Chin-Hui Lee Journal of signal processing systems for signal, image, and video technology . 2018,第7期

机译：基于说话者的基于深度神经网络的单通道联合语音分离和声学建模方法，用于多语音对话的鲁棒识别
3. Distributed Training of Deep Neural Network Acoustic Models for Automatic Speech Recognition: A comparison of current training strategies [J] . Cui Xiaodong, Zhang Wei, Finkler Ulrich, IEEE Signal Processing Magazine . 2020,第3期

机译：自动语音识别深神经网络声学模型的分布式训练：当前训练策略的比较
4. Joint Training of Deep Neural Networks for Multi-Channel Dereverberation and Speech Source Separation [C] . Masahito Togami IEEE International Conference on Acoustics, Speech and Signal Processing . 2020

机译：用于多通道混响和语音源分离的深度神经网络联合训练
5. Supervised speech separation using deep neural networks. [D] . Wang, Yuxuan. 2015

机译：使用深度神经网络监督语音分离。
6. Improving Robustness of Deep Neural Network Acoustic Models via Speech Separation and Joint Adaptive Training [O] . Arun Narayanan, DeLiang Wang -1

机译：通过语音分离和联合自适应训练提高深度神经网络声学模型的鲁棒性
7. Speaker Cluster-Based Speaker Adaptive Training for Deep Neural Network Acoustic Modeling [O] . Chu, Wei, Chen, Ruxin 2016

机译：基于扬声器群的深度神经网络自适应训练声学建模

Improving robustness of deep neural network acoustic models via speech separation and joint adaptive training

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