1 citations · 1 across the 13 of their papers we have counts for
14 papers
SLT 2026 REAL-TSE Challenge: Real-world Target Speaker Extraction from Conversational Recordings
Shuai Wang, Zihan Qian, Ke Zhang +9
We introduce the REAL-TSE Challenge, an IEEE SLT 2026 satellite challenge on target speaker extraction~(TSE) from real conversational recordings. Given a multi-speaker mixture and…
SphereVBx: Spherical Variational Bayes Clustering for Simplified EEND-VC Diarization
Petr Pálka, Jiangyu Han, Prachi Singh +3
We propose SphereVBx, a Bayesian clustering framework for hyperspherical embeddings based on Toroidal Probabilistic Spherical Discriminant Analysis (T-PSDA). The method follows the…
On the Role of Spatial Features in Foundation-Model-Based Speaker Diarization
Marc Deegen, Tobias Gburrek, Tobias Cord-Landwehr +4
Recent advances in speaker diarization exploit large pretrained foundation models, such as WavLM, to achieve state-of-the-art performance on multiple datasets. Systems like DiariZe…
FlexIO: Flexible Single- and Multi-Channel Speech Separation and Enhancement
Yoshiki Masuyama, Kohei Saijo, Francesco Paissan +6
Speech separation and enhancement (SSE) has advanced remarkably and achieved promising results in controlled settings, such as a fixed number of speakers and a fixed array configur…
VBx for End-to-End Neural and Clustering-based Diarization
Petr Pálka, Jiangyu Han, Marc Delcroix +2
We present improvements to speaker diarization in the two-stage end-to-end neural diarization with vector clustering (EEND-VC) framework. The first stage employs a Conformer-based…
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…
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…
BUT System for the MLC-SLM Challenge
Alexander Polok, Jiangyu Han, Dominik Klement +3
We present a two-speaker automatic speech recognition (ASR) system that combines DiCoW -- a diarization-conditioned variant of Whisper -- with DiariZen, a diarization pipeline buil…
Efficient and Robust Speaker Diarization via Structured Pruning of Self-Supervised Models
Jiangyu Han, Petr Pálka, Marc Delcroix +4
This work presents a framework for compressing self-supervised models for speaker diarization through structured pruning guided by knowledge distillation. We investigate pruning ob…
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…
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…
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…
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…