Publication detail
Leveraging Self-Supervised Learning for Speaker Diarization
HAN, J. LANDINI, F. ROHDIN, J. SILNOVA, A. DIEZ SÁNCHEZ, M. BURGET, L.
Original Title
Leveraging Self-Supervised Learning for Speaker Diarization
Type
article in a collection out of WoS and Scopus
Language
English
Original Abstract
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 methods such as WavLM have shown promising performance on several downstream tasks, but their application on speaker diarization is somehow limited. In this work, we explore using WavLM to alleviate the problem of data scarcity for neural diarization training. We use the same pipeline as Pyannote and improve the local end-to-end neural diarization with WavLM and Conformer. Experiments on far-field AMI, AISHELL-4, and AliMeeting datasets show that our method substantially outperforms the Pyannote baseline and achieves new state-of-the-art results on AMI and AISHELL- 4, respectively. In addition, by analyzing the system performance under different data quantity scenarios, we show that WavLM representations are much more robust against data scarcity than filterbank features, enabling less data hungry training strategies. Furthermore, we found that simulated data, usually used to train end-to-end diarization models, does not help when using WavLM in our experiments. Additionally, we also evaluate our model on the recent CHiME8 NOTSOFAR-1 task where it achieves better performance than the Pyannote baseline. Our source code is publicly available at https://github.com/BUTSpeechFIT/DiariZen.
Keywords
Speaker diarization, data scarcity, WavLM, Pyannote, far-field meeting data
Authors
HAN, J.; LANDINI, F.; ROHDIN, J.; SILNOVA, A.; DIEZ SÁNCHEZ, M.; BURGET, L.
Released
6. 4. 2025
Publisher
IEEE Biometric Council
Location
Hyderabad
ISBN
979-8-3503-6874-1
Book
Proceedings of ICASSP 2025
Pages from
1
Pages to
5
Pages count
5
URL
BibTex
@inproceedings{BUT198048,
author="Jiangyu {Han} and Federico Nicolás {Landini} and Johan Andréas {Rohdin} and Anna {Silnova} and Mireia {Diez Sánchez} and Lukáš {Burget}",
title="Leveraging Self-Supervised Learning for Speaker Diarization",
booktitle="Proceedings of ICASSP 2025",
year="2025",
pages="1--5",
publisher="IEEE Biometric Council",
address="Hyderabad",
doi="10.1109/ICASSP49660.2025.10889475",
isbn="979-8-3503-6874-1",
url="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10889475"
}
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