activity
20132024
most citedRecent Developments on ESPnet Toolkit Boosted by Conformer

40 citations · 77 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SD2024★ 22 cited

The VoxCeleb Speaker Recognition Challenge: A Retrospective

Jaesung Huh, Joon Son Chung, Arsha Nagrani +4

The VoxCeleb Speaker Recognition Challenges (VoxSRC) were a series of challenges and workshops that ran annually from 2019 to 2023. The challenges primarily evaluated the tasks of…

eess.AS2023★ 1 cited

VoxWatch: An open-set speaker recognition benchmark on VoxCeleb

Raghuveer Peri, Seyed Omid Sadjadi, Daniel Garcia-Romero

Despite its broad practical applications such as in fraud prevention, open-set speaker identification (OSI) has received less attention in the speaker recognition community compare…

cs.SD2023★ 14 cited

VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge

Jaesung Huh, Andrew Brown, Jee-weon Jung +4

This paper summarises the findings from the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22), which was held in conjunction with INTERSPEECH 2022. The goal of this challenge…

eess.AS2020★ 40 cited

Recent Developments on ESPnet Toolkit Boosted by Conformer

Pengcheng Guo, Florian Boyer, Xuankai Chang +12

In this study, we present recent developments on ESPnet: End-to-End Speech Processing toolkit, which mainly involves a recently proposed architecture called Conformer, Convolution-…

stat.ML2018

Fast variational Bayes for heavy-tailed PLDA applied to i-vectors and x-vectors

Anna Silnova, Niko Brummer, Daniel Garcia-Romero +2

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 involv…

stat.ML2013

Generative Modelling for Unsupervised Score Calibration

Niko Brümmer, Daniel Garcia-Romero

Score calibration enables automatic speaker recognizers to make cost-effective accept / reject decisions. Traditional calibration requires supervised data, which is an expensive re…