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Utterance-Wise Recurrent Dropout and Iterative Speaker Adaptation for Robust Monaural Speech Recognition | IEEE Conference Publication | IEEE Xplore

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 robus...Show More

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.
Date of Conference: 15-20 April 2018
Date Added to IEEE Xplore: 13 September 2018
ISBN Information:
Electronic ISSN: 2379-190X
Conference Location: Calgary, AB, Canada

References

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