Corpus ID: 3264579

Multi-Channel Speech Recognition : LSTMs All the Way Through

@inproceedings{Erdogan2016MultiChannelSR,
  title={Multi-Channel Speech Recognition : LSTMs All the Way Through},
  author={Hakan Erdogan and Tomoki Hayashi and J. Hershey and T. Hori and Chiori Hori and Wei-Ning Hsu and Suyoun Kim and Jonathan Le Roux and Zhong Meng and Shinji Watanabe},
  year={2016}
}
Long Short-Term Memory recurrent neural networks (LSTMs) have demonstrable advantages on a variety of sequential learning tasks. In this paper we demonstrate an LSTM “triple threat” system for speech recognition, where LSTMs drive the three main subsystems: microphone array processing, acoustic modeling, and language modeling. This LSTM trifecta is applied to the CHiME-4 distant recognition challenge. Our previous state-of-the-art ASR systems for the previous CHiME challenge employed LSTM mask… Expand
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References

SHOWING 1-10 OF 26 REFERENCES
The MERL/SRI system for the 3RD CHiME challenge using beamforming, robust feature extraction, and advanced speech recognition
The NTT CHiME-3 system: Advances in speech enhancement and recognition for mobile multi-microphone devices
Recurrent deep neural networks for robust speech recognition
Deep beamforming networks for multi-channel speech recognition
  • X. Xiao, Shinji Watanabe, +7 authors Dong Yu
  • Computer Science, Engineering
  • 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2016
Neural Network Adaptive Beamforming for Robust Multichannel Speech Recognition
End-to-end attention-based large vocabulary speech recognition
KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition
Joint CTC-attention based end-to-end speech recognition using multi-task learning
Towards End-To-End Speech Recognition with Recurrent Neural Networks
...
1
2
3
...