While promising performance for speaker verification has been achieved by deep speaker embeddings, the advantage would reduce in the case of speaking-style variability. Speaking rate mismatch is often observed in practical speaker verification systems, which may actually degrade the system performance. To reduce intra-class discrepancy caused by speaking rate, we propose a deep representation decomposition approach with adversarial learning to learn speaking rate-invariant speaker embeddings. Specifically, adopting an attention block, we decompose the original embedding into identity-related component and rate-related component through multi-task training. Additionally, to reduce the latent relationship between the two decomposed components, we further propose a cosine mapping block to train the parameters adversarially to minimize the cosine similarity between the two decomposed components. As a result, identity-related features become robust to speaking rate and then are used for verification. Experiments are conducted on VoxCeleb1 data and HI-MIA data to demonstrate the effectiveness of our proposed approach.
Cite as: Tong, F., Zheng, S., Zhou, H., Xie, X., Hong, Q., Li, L. (2022) Deep Representation Decomposition for Rate-Invariant Speaker Verification. Proc. The Speaker and Language Recognition Workshop (Odyssey 2022), 228-232, doi: 10.21437/Odyssey.2022-32
@inproceedings{tong22_odyssey,
author={Fuchuan Tong and Siqi Zheng and Haodong Zhou and Xingjia Xie and Qingyang Hong and Lin Li},
title={{Deep Representation Decomposition for Rate-Invariant Speaker Verification}},
year=2022,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2022)},
pages={228--232},
doi={10.21437/Odyssey.2022-32}
}