Leveraging Self-Supervised Learning for Speaker Diarization

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.