SphereVBx adapts VBx clustering to von Mises-Fisher mixtures via T-PSDA for hyperspherical embeddings, yielding comparable or better diarization accuracy with a simpler, sometimes parameter-free, backend.
SphereVBx: Spherical Variational Bayes Clustering for Simplified EEND-VC Diarization
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abstract
We propose SphereVBx, a Bayesian clustering framework for hyperspherical embeddings based on Toroidal Probabilistic Spherical Discriminant Analysis (T-PSDA). The method follows the variational Bayesian formulation of VBx while replacing the Gaussian Probabilistic Linear Discriminant Analysis (PLDA) backend with T-PSDA, resulting in variational inference in a mixture of von Mises-Fisher distributions. We apply SphereVBx to speaker diarization and in particular to the end-to-end neural diarization with vector clustering (EEND-VC) framework. A parameter-free variant, denoted SphereVBx-PF, corresponds to a spherical similarity model closely related to cosine scoring and does not require pretrained backend parameters. Experiments on multiple diarization benchmarks show that SphereVBx improves clustering accuracy in cascaded diarization pipelines and achieves comparable or better performance in the EEND-VC framework while significantly simplifying its clustering stage.
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SphereVBx: Spherical Variational Bayes Clustering for Simplified EEND-VC Diarization
SphereVBx adapts VBx clustering to von Mises-Fisher mixtures via T-PSDA for hyperspherical embeddings, yielding comparable or better diarization accuracy with a simpler, sometimes parameter-free, backend.