79 citations · 105 across the 16 of their papers we have counts for
22 papers
BUT Systems for WildSpoof Challenge: SASV in the Wild
Junyi Peng, Jin Li, Johan Rohdin +3
This paper presents the BUT submission to the WildSpoof Challenge, focusing on the Spoofing-robust Automatic Speaker Verification (SASV) track. We propose a SASV framework designed…
BUT Systems for Environmental Sound Deepfake Detection in the ESDD 2026 Challenge
Junyi Peng, Lin Zhang, Jin Li +2
This paper describes the BUT submission to the ESDD 2026 Challenge, specifically focusing on Track 1: Environmental Sound Deepfake Detection with Unseen Generators. To address the…
Joint Speech and Text Training for LLM-Based End-to-End Spoken Dialogue State Tracking
Katia Vendrame, Bolaji Yusuf, Santosh Kesiraju +3
End-to-end spoken dialogue state tracking (DST) is made difficult by the tandem of having to handle speech input and data scarcity. Combining speech foundation encoders and large l…
TS-SUPERB: A Target Speech Processing Benchmark for Speech Self-Supervised Learning Models
Junyi Peng, Takanori Ashihara, Marc Delcroix +4
Self-supervised learning (SSL) models have significantly advanced speech processing tasks, and several benchmarks have been proposed to validate their effectiveness. However, previ…
Challenging margin-based speaker embedding extractors by using the variational information bottleneck
Themos Stafylakis, Anna Silnova, Johan Rohdin +2
Speaker embedding extractors are typically trained using a classification loss over the training speakers. During the last few years, the standard softmax/cross-entropy loss has be…
Probing Self-supervised Learning Models with Target Speech Extraction
Junyi Peng, Marc Delcroix, Tsubasa Ochiai +4
Large-scale pre-trained self-supervised learning (SSL) models have shown remarkable advancements in speech-related tasks. However, the utilization of these models in complex multi-…
Target Speech Extraction with Pre-trained Self-supervised Learning Models
Junyi Peng, Marc Delcroix, Tsubasa Ochiai +3
Pre-trained self-supervised learning (SSL) models have achieved remarkable success in various speech tasks. However, their potential in target speech extraction (TSE) has not been…
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…
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…
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…
MultiSV: Dataset for Far-Field Multi-Channel Speaker Verification
Ladislav Mošner, Oldřich Plchot, Lukáš Burget +1
Motivated by unconsolidated data situation and the lack of a standard benchmark in the field, we complement our previous efforts and present a comprehensive corpus designed for tra…
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…
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…
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…
BUT VOiCES 2019 System Description
Hossein Zeinali, Pavel Matějka, Ladislav Mošner +6
This is a description of our effort in VOiCES 2019 Speaker Recognition challenge. All systems in the fixed condition are based on the x-vector paradigm with different features and…
Self-supervised speaker embeddings
Themos Stafylakis, Johan Rohdin, Oldrich Plchot +2
Contrary to i-vectors, speaker embeddings such as x-vectors are incapable of leveraging unlabelled utterances, due to the classification loss over training speakers. In this paper,…
Factorization of Discriminatively Trained i-vector Extractor for Speaker Recognition
Ondrej Novotny, Oldrich Plchot, Ondrej Glembek +1
In this work, we continue in our research on i-vector extractor for speaker verification (SV) and we optimize its architecture for fast and effective discriminative training. We we…
Analysis of DNN Speech Signal Enhancement for Robust Speaker Recognition
Ondrej Novotny, Oldrich Plchot, Ondrej Glembek +2
In this work, we present an analysis of a DNN-based autoencoder for speech enhancement, dereverberation and denoising. The target application is a robust speaker verification (SV)…
On the use of DNN Autoencoder for Robust Speaker Recognition
Ondrej Novotny, Oldrich Plchot, Pavel Matejka +1
In this paper, we present an analysis of a DNN-based autoencoder for speech enhancement, dereverberation and denoising. The target application is a robust speaker recognition syste…
Speaker verification using end-to-end adversarial language adaptation
Johan Rohdin, Themos Stafylakis, Anna Silnova +3
In this paper we investigate the use of adversarial domain adaptation for addressing the problem of language mismatch between speaker recognition corpora. In the context of speaker…
Discriminatively Re-trained i-vector Extractor for Speaker Recognition
Ondrej Novotny, Oldrich Plchot, Ondrej Glembek +2
In this work we revisit discriminative training of the i-vector extractor component in the standard speaker verification (SV) system. The motivation of our research lies in the rob…
End-to-end DNN Based Speaker Recognition Inspired by i-vector and PLDA
Johan Rohdin, Anna Silnova, Mireia Diez +3
Recently several end-to-end speaker verification systems based on deep neural networks (DNNs) have been proposed. These systems have been proven to be competitive for text-dependen…