• 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 John R. 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… 

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