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-8.1
Paper Title SQUEEZING VALUE OF CROSS-DOMAIN LABELS: A DECOUPLED SCORING APPROACH FOR SPEAKER VERIFICATION
Authors Lantian Li, Yang Zhang, Jiawen Kang, Thomas Fang Zheng, Dong Wang, Tsinghua 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 Domain mismatch is often occurred in real applications and causes serious performance reduction on speaker recognition systems. The common wisdom is to collect cross-domain data and train a multi-domain PLDA model, with the hope to learn a domain-independent speaker subspace. In this paper, we firstly present an empirical study to show that simply adding cross-domain data does not help performance in conditions with enroll-test mismatch. Careful analysis shows that this striking result is caused by the incoherent statistics between enroll and test conditions. Based on this analysis, we present a decoupled scoring approach that can maximally squeeze the value of cross-domain labels and obtain optimal verification scores in the enrollment-test mismatch condition. When the statistics are coherent, the new formulation falls back to the conventional PLDA. Experimental results on cross-channel test show that the proposed approach is highly effective and is a principal solution to domain mismatch.