2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPE-8.3
Paper Title META-LEARNING FOR CROSS-CHANNEL SPEAKER VERIFICATION
Authors Hanyi Zhang, Longbiao Wang, College of Intelligence and Computing, Tianjin University, China; Kong Aik Lee, Institute for Infocomm Research, A*STAR, Singapore; Meng Liu, Jianwu Dang, Hui Chen, College of Intelligence and Computing, Tianjin University, China
SessionSPE-8: Speaker Recognition 2: Channel and Domain Robustness
LocationGather.Town
Session Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Poster
Topic Speech Processing: [SPE-SPKR] Speaker Recognition and Characterization
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.