19 citations · 23 across the 17 of their papers we have counts for
19 papers
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…
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…
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…
From Modular to End-to-End Speaker Diarization
Federico Landini
Speaker diarization is usually referred to as the task that determines ``who spoke when'' in a recording. Until a few years ago, all competitive approaches were modular. Systems ba…
Spoof Diarization: "What Spoofed When" in Partially Spoofed Audio
Lin Zhang, Xin Wang, Erica Cooper +4
This paper defines Spoof Diarization as a novel task in the Partial Spoof (PS) scenario. It aims to determine what spoofed when, which includes not only locating spoof regions but…
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…
DiaPer: End-to-End Neural Diarization with Perceiver-Based Attractors
Federico Landini, Mireia Diez, Themos Stafylakis +1
Until recently, the field of speaker diarization was dominated by cascaded systems. Due to their limitations, mainly regarding overlapped speech and cumbersome pipelines, end-to-en…
Discriminative Training of VBx Diarization
Dominik Klement, Mireia Diez, Federico Landini +4
Bayesian HMM clustering of x-vector sequences (VBx) has become a widely adopted diarization baseline model in publications and challenges. It uses an HMM to model speaker turns, a…
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…
Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization
Marc Delcroix, Naohiro Tawara, Mireia Diez +6
Combining end-to-end neural speaker diarization (EEND) with vector clustering (VC), known as EEND-VC, has gained interest for leveraging the strengths of both methods. EEND-VC esti…
Multi-Speaker and Wide-Band Simulated Conversations as Training Data for End-to-End Neural Diarization
Federico Landini, Mireia Diez, Alicia Lozano-Diez +1
End-to-end diarization presents an attractive alternative to standard cascaded diarization systems because a single system can handle all aspects of the task at once. Many flavors…
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…
Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks
Federico Landini, Ján Profant, Mireia Diez +1
The recently proposed VBx diarization method uses a Bayesian hidden Markov model to find speaker clusters in a sequence of x-vectors. In this work we perform an extensive compariso…
Analysis of the BUT Diarization System for VoxConverse Challenge
Federico Landini, Ondřej Glembek, Pavel Matějka +4
This paper describes the system developed by the BUT team for the fourth track of the VoxCeleb Speaker Recognition Challenge, focusing on diarization on the VoxConverse dataset. Th…
BUT System for the Second DIHARD Speech Diarization Challenge
Federico Landini, Shuai Wang, Mireia Diez +9
This paper describes the winning 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 mainly…
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…