activity
20172022
most citedBUT System Description to VoxCeleb Speaker Recognition Challenge 2019

79 citations · 84 across the 6 of their papers we have counts for

collaborators

14 papers

eess.AS20221 cited

Extracting speaker and emotion information from self-supervised speech models via channel-wise correlations

Themos Stafylakis, Ladislav Mosner, Sofoklis Kakouros +3

Self-supervised learning of speech representations from large amounts of unlabeled data has enabled state-of-the-art results in several speech processing tasks. Aggregating these s…

eess.AS20221 cited

An attention-based backend allowing efficient fine-tuning of transformer models for speaker verification

Junyi Peng, Oldrich Plchot, Themos Stafylakis +3

In recent years, self-supervised learning paradigm has received extensive attention due to its great success in various down-stream tasks. However, the fine-tuning strategies for a…

eess.AS20221 cited

Analyzing speaker verification embedding extractors and back-ends under language and channel mismatch

Anna Silnova, Themos Stafylakis, Ladislav Mosner +6

In this paper, we analyze the behavior and performance of speaker embeddings and the back-end scoring model under domain and language mismatch. We present our findings regarding Re…

eess.AS2020

BUT System for the Second DIHARD Speech Diarization Challenge

Federico Landini, Shuai Wang, Mireia Diez +9

This paper describes the winning systems developed by the BUT team for the four tracks of the Second DIHARD Speech Diarization Challenge. For tracks 1 and 2 the systems were mainly…

eess.AS201979 cited

BUT System Description to VoxCeleb Speaker Recognition Challenge 2019

Hossein Zeinali, Shuai Wang, Anna Silnova +2

In this report, we describe the submission of Brno University of Technology (BUT) team to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2019. We also provide a brief analysis…

cs.CL2019

Learning document embeddings along with their uncertainties

Santosh Kesiraju, Oldřich Plchot, Lukáš Burget +1

Majority of the text modelling techniques yield only point-estimates of document embeddings and lack in capturing the uncertainty of the estimates. These uncertainties give a notio…