6 citations · 16 across the 10 of their papers we have counts for
16 papers · 1 filter
DiCoW: Diarization-Conditioned Whisper for Target Speaker Automatic Speech Recognition
Alexander Polok, Dominik Klement, Martin Kocour +7
Speaker-attributed automatic speech recognition (ASR) in multi-speaker environments remains a significant challenge, particularly when systems conditioned on speaker embeddings fai…
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…
DPCCN: Densely-Connected Pyramid Complex Convolutional Network for Robust Speech Separation And Extraction
Jiangyu Han, Yanhua Long, Lukas Burget +1
In recent years, a number of time-domain speech separation methods have been proposed. However, most of them are very sensitive to the environments and wide domain coverage tasks.…
EAT: Enhanced ASR-TTS for Self-supervised Speech Recognition
Murali Karthick Baskar, Lukáš Burget, Shinji Watanabe +2
Self-supervised ASR-TTS models suffer in out-of-domain data conditions. Here we propose an enhanced ASR-TTS (EAT) model that incorporates two main features: 1) The ASR…
Integration of variational autoencoder and spatial clustering for adaptive multi-channel neural speech separation
Katerina Zmolikova, Marc Delcroix, Lukáš Burget +2
In this paper, we propose a method combining variational autoencoder model of speech with a spatial clustering approach for multi-channel speech separation. The advantage of integr…