Deep Representation Decomposition for Rate-Invariant Speaker Verification
Fuchuan Tong, Siqi Zheng, Haodong Zhou, Xingjia Xie, Qingyang Hong, Lin Li · 2022
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.