Utterance-Wise Recurrent Dropout and Iterative Speaker Adaptation for Robust Monaural Speech Recognition

Peidong Wang; DeLiang Wang

ICASSP 2018accepted0 citations

Utterance-Wise Recurrent Dropout and Iterative Speaker Adaptation for Robust Monaural Speech Recognition

Abstract

This study addresses monaural (single-microphone) automatic speech recognition (ASR) in adverse acoustic conditions. Our study builds on a state-of-the-art monaural robust ASR method that uses a wide residual network with bidirectional long short-term memory (BLSTM). We propose a novel utterance-wise dropout method for training LSTM networks and an iterative speaker adaptation technique. When evaluated on the monaural speech recognition task of the CHiME-4 corpus, our model yields a word error rate (WER) of 8.28% using the baseline language model, outperforming the previous best monaural ASR by 16.19% relatively.

BibTeX

@inproceedings{icassp2018_utterancewiserec,
  title = {Utterance-Wise Recurrent Dropout and Iterative Speaker Adaptation for Robust Monaural Speech Recognition},
  author = {Peidong Wang and DeLiang Wang},
  booktitle = {ICASSP 2018},
  year = {2018}
}