19 citations · 28 across the 26 of their papers we have counts for
7 papers · 1 filter
Joint Training of Speaker Embedding Extractor, Speech and Overlap Detection for Diarization
Petr Pálka, Federico Landini, Dominik Klement +4
In spite of the popularity of end-to-end diarization systems nowadays, modular systems comprised of voice activity detection (VAD), speaker embedding extraction plus clustering, an…
Improving Automatic Speech Recognition with Decoder-Centric Regularisation in Encoder-Decoder Models
Alexander Polok, Santosh Kesiraju, Karel Beneš +2
This paper proposes a simple yet effective way of regularising the encoder-decoder-based automatic speech recognition (ASR) models that enhance the robustness of the model and impr…
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…
Beyond the Labels: Unveiling Text-Dependency in Paralinguistic Speech Recognition Datasets
Jan Pešán, Santosh Kesiraju, Lukáš Burget +1
Paralinguistic traits like cognitive load and emotion are increasingly recognized as pivotal areas in speech recognition research, often examined through specialized datasets like…
Do End-to-End Neural Diarization Attractors Need to Encode Speaker Characteristic Information?
Lin Zhang, Themos Stafylakis, Federico Landini +3
In this paper, we apply the variational information bottleneck approach to end-to-end neural diarization with encoder-decoder attractors (EEND-EDA). This allows us to investigate w…