A speech enhancement algorithm by iterating single- and multi-microphone processing and its application to robust ASR
Xueliang Zhang, Zhong-Qiu Wang, DeLiang Wang
Abstract
We propose a speech enhancement algorithm based on single- and multi-microphone processing techniques. The core of the algorithm estimates a time-frequency mask which represents the target speech and use masking-based beamforming to enhance corrupted speech. Specifically, in single-microphone processing, the received signals of a microphone array are treated as individual signals and we estimate a mask for the signal of each microphone using a deep neural network (DNN). With these masks, in multi-microphone processing, we calculate a spatial covariance matrix of noise and steering vector for beamforming. In addition, we propose a masking-based post-filter to further suppress the noise in the output of beamforming. Then, the enhanced speech is sent back to DNN for mask re-estimation. When these steps are iterated for a few times, we obtain the final enhanced speech. The proposed algorithm is evaluated as a frontend for automatic speech recognition (ASR) and achieves a 5.05% average word error rate (WER) on the real environment test set of CHiME-3, outperforming the current best algorithm by 13.34%.
BibTeX
@inproceedings{icassp2017_aspeechenhanceme,
title = {A speech enhancement algorithm by iterating single- and multi-microphone processing and its application to robust ASR},
author = {Xueliang Zhang and Zhong-Qiu Wang and DeLiang Wang},
booktitle = {ICASSP 2017},
year = {2017}
}