Publication Details
Probabilistic Spherical Discriminant Analysis: An Alternative to PLDA for length-normalized embeddings
Swart Albert du Preez (Speechly)
Mošner Ladislav, Ing. (DCGM FIT BUT)
Silnova Anna, MSc., Ph.D. (DCGM FIT BUT)
Plchot Oldřich, Ing., Ph.D. (DCGM FIT BUT)
Stafylakis Themos (OMILIA)
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT)
speaker recognition, PSDA, Von Mises-Fisher
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 mixedlength 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 multienroll 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.
@INPROCEEDINGS{FITPUB12845,
author = "Langenhoven Nikolaas Johan Brummer and Preez du Albert Swart and Ladislav Mo\v{s}ner and Anna Silnova and Old\v{r}ich Plchot and Themos Stafylakis and Luk\'{a}\v{s} Burget",
title = "Probabilistic Spherical Discriminant Analysis: An Alternative to PLDA for length-normalized embeddings",
pages = "1446--1450",
booktitle = "Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",
journal = "Proceedings of Interspeech - on-line",
volume = 2022,
number = 9,
year = 2022,
location = "Incheon, KR",
publisher = "International Speech Communication Association",
ISSN = "1990-9772",
doi = "10.21437/Interspeech.2022-731",
language = "english",
url = "https://www.fit.vut.cz/research/publication/12845"
}