In this paper, we present a novel architecture for multichannel speech enhancement using a cross-channel attention-based Wave-U-Net structure. Despite the advantages of utilizing spatial information as well as spectral information, it is challenging to effectively train a multi-channel deep learning system in an end-to-end framework. With a channel-independent encoding architecture for spectral estimation and a strategy to extract spatial information through an inter-channel attention mechanism, we implement a multi-channel speech enhancement system that has high performance even in reverberant and extremely noisy environments. Experimental results show that the proposed architecture has superior performance in terms of signal-to-distortion ratio improvement (SDRi), short-time objective intelligence (STOI), and phoneme error rate (PER) for speech recognition.
|Number of pages||5|
|Journal||Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH|
|Publication status||Published - 2020|
|Event||21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 - Shanghai, China|
Duration: 2020 Oct 25 → 2020 Oct 29
Bibliographical noteFunding Information:
This work is supported and funded by LG Electronics Co., Ltd.
Copyright © 2020 ISCA
All Science Journal Classification (ASJC) codes
- Language and Linguistics
- Human-Computer Interaction
- Signal Processing
- Modelling and Simulation