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stat.ML2022
Probabilistic Spherical Discriminant Analysis: An Alternative to PLDA for length-normalized embeddings
Niko Brümmer, Albert Swart, Ladislav Mošner +4
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 adv…
stat.ML2018
Gaussian meta-embeddings for efficient scoring of a heavy-tailed PLDA model
Niko Brummer, Anna Silnova, Lukas Burget +1
Embeddings in machine learning are low-dimensional representations of complex input patterns, with the property that simple geometric operations like Euclidean distances and dot pr…