Target speech extraction has attracted widespread attention. When microphone arrays are available, the additional spatial information can be helpful in extracting the target speech. We have recently proposed a channel decorrelation (CD) mechanism to extract the inter-channel differential information to enhance the reference channel encoder representation. Although the proposed mechanism has shown promising results for extracting the target speech from mixtures, the extraction performance is still limited by the nature of the original decorrelation theory. In this paper, we propose two methods to broaden the horizon of the original channel decorrelation, by replacing the original softmax-based inter-channel similarity between encoder representations, using an unrolled probability and a normalized cosine-based similarity at the dimensional-level. Moreover, new combination strategies of the CD-based spatial information and target speaker adaptation of parallel encoder outputs are also investigated. Experiments on the reverberant WSJ0 2-mix show that the improved CD can result in more discriminative differential information and the new adaptation strategy is also very effective to improve the target speech extraction.
Cite as: Han, J., Rao, W., Wang, Y., Long, Y. (2021) Improving Channel Decorrelation for Multi-Channel Target Speech Extraction. Proc. Interspeech 2021, 1847-1851, doi: 10.21437/Interspeech.2021-298
@inproceedings{han21b_interspeech,
author={Jiangyu Han and Wei Rao and Yannan Wang and Yanhua Long},
title={{Improving Channel Decorrelation for Multi-Channel Target Speech Extraction}},
year=2021,
booktitle={Proc. Interspeech 2021},
pages={1847--1851},
doi={10.21437/Interspeech.2021-298}
}