Detail publikace

Toroidal Probabilistic Spherical Discriminant Analysis

SILNOVA, A. BRUMMER, J. SWART, A. BURGET, L.

Originální název

Toroidal Probabilistic Spherical Discriminant Analysis

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

speaker recognition, PSDA, Von Mises-Fishe

Autoři

SILNOVA, A.; BRUMMER, J.; SWART, A.; BURGET, L.

Vydáno

4. 6. 2023

Nakladatel

IEEE Signal Processing Society

Místo

Rhodes Island

ISBN

978-1-7281-6327-7

Kniha

Proceedings of ICASSP 2023

Strany od

1

Strany do

5

Strany počet

5

URL

https://ieeexplore.ieee.org/document/10095580

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"
}