19 citations · 27 across the 15 of their papers we have counts for
15 papers
How to Leverage Synthetic Speech for LLM-Based ASR Systems?
Yanis Labrak, Dairazalia Sanchez-Cortes, Sergio Burdisso +9
In regulated domains such as banking and healthcare, where privacy constraints make real speech costly to collect and retain, synthetic speech from modern text-to-speech (TTS) is a…
SpeakerCard-1M: An Evidence-Grounded Corpus for In-the-Wild Speaker Verification
Junyi Peng, Oldřich Plchot, Xiao Song +9
Modern speaker verification (SV) systems rely on speaker embeddings that are effective but difficult to interpret or query in natural language. Most existing speech-text corpora ta…
FLiP: Towards understanding and interpreting multimodal multilingual sentence embeddings
Santosh Kesiraju, Bolaji Yusuf, Šimon Sedláček +2
This paper presents factorized linear projection (FLiP) models for understanding pretrained sentence embedding spaces. We train FLiP models to recover the lexical content from mult…
Hybrid Pruning: In-Situ Compression of Self-Supervised Speech Models for Speaker Verification and Anti-Spoofing
Junyi Peng, Lin Zhang, Jiangyu Han +5
Although large-scale self-supervised learning (SSL) models like WavLM have achieved state-of-the-art performance in speech processing, their significant size impedes deployment on…
Factors affecting the in-context learning abilities of LLMs for dialogue state tracking
Pradyoth Hegde, Santosh Kesiraju, Jan Švec +5
This study explores the application of in-context learning (ICL) to the dialogue state tracking (DST) problem and investigates the factors that influence its effectiveness. We use…
Approaching Dialogue State Tracking via Aligning Speech Encoders and LLMs
Šimon Sedláček, Bolaji Yusuf, Ján Švec +4
In this work, we approach spoken Dialogue State Tracking (DST) by bridging the representation spaces of speech encoders and LLMs via a small connector module, with a focus on fully…
Analysis of ABC Frontend Audio Systems for the NIST-SRE24
Sara Barahona, Anna Silnova, Ladislav Mošner +14
We present a comprehensive analysis of the embedding extractors (frontends) developed by the ABC team for the audio track of NIST SRE 2024. We follow the two scenarios imposed by N…
State-of-the-art Embeddings with Video-free Segmentation of the Source VoxCeleb Data
Sara Barahona, Ladislav Mošner, Themos Stafylakis +4
In this paper, we refine and validate our method for training speaker embedding extractors using weak annotations. More specifically, we use only the audio stream of the source Vox…
CA-MHFA: A Context-Aware Multi-Head Factorized Attentive Pooling for SSL-Based Speaker Verification
Junyi Peng, Ladislav Mošner, Lin Zhang +4
Self-supervised learning (SSL) models for speaker verification (SV) have gained significant attention in recent years. However, existing SSL-based SV systems often struggle to capt…
BUT Systems and Analyses for the ASVspoof 5 Challenge
Johan Rohdin, Lin Zhang, Oldřich Plchot +8
This paper describes the BUT submitted systems for the ASVspoof 5 challenge, along with analyses. For the conventional deepfake detection task, we use ResNet18 and self-supervised…
Improving Speaker Verification with Self-Pretrained Transformer Models
Junyi Peng, Oldřich Plchot, Themos Stafylakis +3
Recently, fine-tuning large pre-trained Transformer models using downstream datasets has received a rising interest. Despite their success, it is still challenging to disentangle t…
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…
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…
Training Speaker Embedding Extractors Using Multi-Speaker Audio with Unknown Speaker Boundaries
Themos Stafylakis, Ladislav Mošner, Oldřich Plchot +4
In this paper, we demonstrate a method for training speaker embedding extractors using weak annotation. More specifically, we are using the full VoxCeleb recordings and the name of…
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…