Meta-Learning for Cross-Channel Speaker Verification

Meta-Learning for Cross-Channel Speaker Verification
Title:
Meta-Learning for Cross-Channel Speaker Verification
Other Titles:
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publication Date:
13 May 2021
Citation:
Zhang, H., Wang, L., Lee, K. A., Liu, M., Dang, J., Chen, H. (2021). Meta-Learning for Cross-Channel Speaker Verification. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp39728.2021.9413978
Abstract:
Automatic speaker verification (ASV) has been successfully deployed for identity recognition. With increasing use of ASV technology in real-world applications, channel mismatch caused by the recording devices and environments severely degrade its performance, especially in the case of unseen channels. To this end, we propose a meta speaker embedding network (MSEN) via meta-learning to generate channel-invariant utterance embeddings. Specifically, we optimize the differences between the embeddings of a support set and a query set in order to learn a channel-invariant embedding space for utterances. Furthermore, we incorporate distribution optimization (DO) to stabilize the performance of MSEN. To quantitatively measure the effect of MSEN on unseen channels, we specially design the generalized cross-channel (GCC) evaluation. The experimental results on the HI-MIA corpus demonstrate that the proposed MSEN reduce considerably the impact of channel mismatch, while significantly outperforms other state-of-the-art methods.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Key R&D Program of China - -
Grant Reference no. : 2018YFB1305200

This research / project is supported by the National Natural Science Foundation of China - -
Grant Reference no. : 61771333

This research / project is supported by the Tianjin Municipal Science and Technology Project - -
Grant Reference no. : 18ZXZNGX00330
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2379-190X
1520-6149
ISBN:
978-1-7281-7605-5
978-1-7281-7606-2
Files uploaded:

File Size Format Action
zhang.pdf 2.13 MB PDF Request a copy