Probabilistic Spherical Discriminant Analysis: An Alternative to PLDA for length-normalized embeddingsNiko Brummer, Albert Swart, Ladislav Mosner, Anna Silnova, Oldrich Plchot, Themos Stafylakis, Lukas Burget

PHONEXIA, South Africa
Brno University of Technology, Speech@FIT and IT4I Center of Excellence, Brno, Czechia
Omilia - Conversational Intelligence, Athens, Greece


In speaker recognition, where speech segments are mapped to embeddings on the unit hypersphere, two scoring backends are commonly used, namely cosine scoring or PLDA. Both have advantages and disadvantages, depending on the context. Cosine scoring follows naturally from the spherical geometry, but for PLDA the blessing is mixed – length normalization Gaussianizes the between-speaker distribution, but violates the assumption of a speaker-independent within-speaker distribution.

We propose PSDA, an analogue to PLDA that uses Von Mises-Fisher distributions on the hypersphere for both within and between-class distributions. We show how the self-conjugacy of this distribution gives closed-form likelihood-ratio scores, making it a drop-in replacement for PLDA at scoring time. All kinds of trials can be scored, including single-enroll and multi-enroll verification, as well as more complex likelihood-ratios that could be used in clustering and diarization. Learning is done via an EM-algorithm with closed-form updates. We explain the model and present some first experiments.



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