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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