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 MoreMetadata
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.
Published in: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 15-20 April 2018
Date Added to IEEE Xplore: 13 September 2018
ISBN Information:
Electronic ISSN: 2379-190X