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arXiv · 2610.06691

Revisiting Label-Free Speaker Embedding Enhancement with vMF Profile Likelihood

Seunghwan Kim·Jinyong Kim·Sooyoung Yang·Youngjin Ko·Myungjoo Kang
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Abstract

Embedding enhancement improves speaker verification under acoustic mismatch without modifying a frozen backbone. Recent work has established a practical label-free setting for this task, but often adopts increasingly structured formulations. Here, the clean target is directly observed during training, making enhancement a matching problem on the unit hypersphere. We model the clean target with a von Mises--Fisher (vMF) likelihood and profile out a sample-wise concentration parameter, yielding a simple closed-form objective with adaptive weighting. Across VoxCeleb1, VoxSRC23, CN-Celeb, VOiCES, and VC-Mix, the proposed method largely preserves the baseline and gives clearer gains on challenging mismatch sets. It also remains stable under a broad single-view recipe, where a recent diffusion baseline becomes less reliable in controlled comparisons. These results suggest that effective label-free embedding enhancement in this setting does not require a highly structured formulation.

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Seunghwan Kim, Jinyong Kim, Sooyoung Yang, Youngjin Ko, Myungjoo Kang. 2026-10-05. Revisiting Label-Free Speaker Embedding Enhancement with vMF Profile Likelihood. https://doi.org/10.21437/interspeech.2026-1146

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