Publication detail
Toroidal Probabilistic Spherical Discriminant Analysis
SILNOVA, A. BRUMMER, J. SWART, A. BURGET, L.
Original Title
Toroidal Probabilistic Spherical Discriminant Analysis
Type
conference paper
Language
English
Original Abstract
n speaker recognition, where speech segments are mapped to embeddings on the unit hypersphere, two scoring back-ends are commonly used, namely cosine scoring and PLDA. We have recently proposed PSDA, an analog to PLDA that uses Von Mises-Fisher distributions instead of Gaussians. In this paper, we present toroidal PSDA (T-PSDA). It extends PSDA with the ability to model within and between-speaker variabilities in toroidal submanifolds of the hypersphere. Like PLDA and PSDA, the model allows closed-form scoring and closed-form EM updates for training. On VoxCeleb, we find T-PSDA accu- racy on par with cosine scoring, while PLDA accuracy is infe- rior. On NIST SRE'21 we find that T-PSDA gives large accu- racy gains compared to both cosine scoring and PLDA.
Keywords
speaker recognition, PSDA, Von Mises-Fishe
Authors
SILNOVA, A.; BRUMMER, J.; SWART, A.; BURGET, L.
Released
4. 6. 2023
Publisher
IEEE Signal Processing Society
Location
Rhodes Island
ISBN
978-1-7281-6327-7
Book
Proceedings of ICASSP 2023
Pages from
1
Pages to
5
Pages count
5
URL
BibTex
@inproceedings{BUT185199,
author="Anna {Silnova} and Johan Nikolaas Langenhoven {Brummer} and Albert du Preez {Swart} and Lukáš {Burget}",
title="Toroidal Probabilistic Spherical Discriminant Analysis",
booktitle="Proceedings of ICASSP 2023",
year="2023",
pages="1--5",
publisher="IEEE Signal Processing Society",
address="Rhodes Island",
doi="10.1109/ICASSP49357.2023.10095580",
isbn="978-1-7281-6327-7",
url="https://ieeexplore.ieee.org/document/10095580"
}Documents