activity
20192024
most citedBUT System Description for DIHARD Speech Diarization Challenge 2019

19 citations · 19 across the 4 of their papers we have counts for

collaborators

5 papers

eess.AS2022

Parameter-efficient transfer learning of pre-trained Transformer models for speaker verification using adapters

Junyi Peng, Themos Stafylakis, Rongzhi Gu +4

Recently, the pre-trained Transformer models have received a rising interest in the field of speech processing thanks to their great success in various downstream tasks. However, m…

stat.ML2022

Probabilistic Spherical Discriminant Analysis: An Alternative to PLDA for length-normalized embeddings

Niko Brümmer, Albert Swart, Ladislav Mošner +4

In speaker recognition, where speech segments are mapped to embeddings on the unit hypersphere, two scoring backends are commonly used, namely cosine scoring or PLDA. Both have adv…

eess.AS2020

Probabilistic embeddings for speaker diarization

Anna Silnova, Niko Brümmer, Johan Rohdin +2

Speaker embeddings (x-vectors) extracted from very short segments of speech have recently been shown to give competitive performance in speaker diarization. We generalize this reci…

eess.AS201919 cited

BUT System Description for DIHARD Speech Diarization Challenge 2019

Federico Landini, Shuai Wang, Mireia Diez +8

This paper describes the 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 based on perfo…

cs.CL2019

BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers

Martin Fajcik, Lukáš Burget, Pavel Smrz

This paper describes our system submitted to SemEval 2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell et al., 2019). The challe…