1 citations · 2 across the 11 of their papers we have counts for
12 papers
SE-DiCoW: Self-Enrolled Diarization-Conditioned Whisper
Alexander Polok, Dominik Klement, Samuele Cornell +4
Speaker-attributed automatic speech recognition (ASR) in multi-speaker environments remains a major challenge. While some approaches achieve strong performance when fine-tuned on s…
Spatially Aware Self-Supervised Models for Multi-Channel Neural Speaker Diarization
Jiangyu Han, Ruoyu Wang, Yoshiki Masuyama +4
Self-supervised models such as WavLM have demonstrated strong performance for neural speaker diarization. However, these models are typically pre-trained on single-channel recordin…
Adapting Diarization-Conditioned Whisper for End-to-End Multi-Talker Speech Recognition
Martin Kocour, Martin Karafiat, Alexander Polok +3
We propose a speaker-attributed (SA) Whisper-based model for multi-talker speech recognition that combines target-speaker modeling with serialized output training (SOT). Our approa…
Unsupervised Speech Enhancement using Data-defined Priors
Dominik Klement, Matthew Maciejewski, Sanjeev Khudanpur +2
The majority of deep learning-based speech enhancement methods require paired clean-noisy speech data. Collecting such data at scale in real-world conditions is infeasible, which h…
Fine-tune Before Structured Pruning: Towards Compact and Accurate Self-Supervised Models for Speaker Diarization
Jiangyu Han, Federico Landini, Johan Rohdin +4
Self-supervised learning (SSL) models like WavLM can be effectively utilized when building speaker diarization systems but are often large and slow, limiting their use in resource…
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…
Leveraging Self-Supervised Learning for Speaker Diarization
Jiangyu Han, Federico Landini, Johan Rohdin +3
End-to-end neural diarization has evolved considerably over the past few years, but data scarcity is still a major obstacle for further improvements. Self-supervised learning metho…
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…
Text-dependent Speaker Verification (TdSV) Challenge 2024: Challenge Evaluation Plan
Zeinali Hossein, Lee Kong Aik, Alam Jahangir +1
This document outlines the Text-dependent Speaker Verification (TdSV) Challenge 2024, which centers on analyzing and exploring novel approaches for text-dependent speaker verificat…
DiaCorrect: Error Correction Back-end For Speaker Diarization
Jiangyu Han, Federico Landini, Johan Rohdin +5
In this work, we propose an error correction framework, named DiaCorrect, to refine the output of a diarization system in a simple yet effective way. This method is inspired by err…
From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization
Federico Landini, Alicia Lozano-Diez, Mireia Diez +1
End-to-end neural diarization (EEND) is nowadays one of the most prominent research topics in speaker diarization. EEND presents an attractive alternative to standard cascaded diar…
Residual Memory Networks: Feed-forward approach to learn long temporal dependencies
Murali Karthick Baskar, Martin Karafiat, Lukas Burget +3
Training deep recurrent neural network (RNN) architectures is complicated due to the increased network complexity. This disrupts the learning of higher order abstracts using deep R…