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SILNOVA, A. BRUMMER, J. GARCÍA-ROMERO, D. SNYDER, D. BURGET, L.
Original Title
Fast variational Bayes for heavy-tailed PLDA applied to i-vectors and x-vectors
Type
conference paper
Language
English
Original Abstract
The standard state-of-the-art backend for text-independent speaker recognizers that use i-vectors or x-vectors, is Gaussian PLDA (G-PLDA), assisted by a Gaussianization step involving length normalization. G-PLDA can be trained with both generative or discriminative methods. It has long been known that heavy-tailed PLDA (HT-PLDA), applied without length normalization, gives similar accuracy, but at considerable extra computational cost. We have recently introduced a fast scoring algorithm for a discriminatively trained HT-PLDA backend. This paper extends that work by introducing a fast, variational Bayes, generative training algorithm. We compare old and new backends, with and without length-normalization, with i-vectors and x-vectors, on SRE10, SRE16 and SITW.
Keywords
peaker recognition, variational Bayes, heavytailed PLDA
Authors
SILNOVA, A.; BRUMMER, J.; GARCÍA-ROMERO, D.; SNYDER, D.; BURGET, L.
Released
2. 9. 2018
Publisher
International Speech Communication Association
Location
Hyderabad
ISBN
1990-9772
Periodical
Proceedings of Interspeech
Year of study
2018
Number
9
State
French Republic
Pages from
72
Pages to
76
Pages count
5
URL
https://www.isca-speech.org/archive/Interspeech_2018/abstracts/2128.html
BibTex
@inproceedings{BUT155098, author="SILNOVA, A. and BRUMMER, J. and GARCÍA-ROMERO, D. and SNYDER, D. and BURGET, L.", title="Fast variational Bayes for heavy-tailed PLDA applied to i-vectors and x-vectors", booktitle="Proceedings of Interspeech 2018", year="2018", journal="Proceedings of Interspeech", volume="2018", number="9", pages="72--76", publisher="International Speech Communication Association", address="Hyderabad", doi="10.21437/Interspeech.2018-2128", issn="1990-9772", url="https://www.isca-speech.org/archive/Interspeech_2018/abstracts/2128.html" }