ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

Adapting Speaker Embeddings for Speaker Diarisation

Youngki Kwon, Jee-weon Jung, Hee-Soo Heo, You Jin Kim, Bong-Jin Lee, Joon Son Chung

The goal of this paper is to adapt speaker embeddings for solving the problem of speaker diarisation. The quality of speaker embeddings is paramount to the performance of speaker diarisation systems. Despite this, prior works in the field have directly used embeddings designed only to be effective on the speaker verification task. In this paper, we propose three techniques that can be used to better adapt the speaker embeddings for diarisation: dimensionality reduction, attention-based embedding aggregation, and non-speech clustering. A wide range of experiments is performed on various challenging datasets. The results demonstrate that all three techniques contribute positively to the performance of the diarisation system achieving an average relative improvement of 25.07% in terms of diarisation error rate over the baseline.

doi: 10.21437/Interspeech.2021-448

Cite as: Kwon, Y., Jung, J.-w., Heo, H.-S., Kim, Y.J., Lee, B.-J., Chung, J.S. (2021) Adapting Speaker Embeddings for Speaker Diarisation. Proc. Interspeech 2021, 3101-3105, doi: 10.21437/Interspeech.2021-448

  author={Youngki Kwon and Jee-weon Jung and Hee-Soo Heo and You Jin Kim and Bong-Jin Lee and Joon Son Chung},
  title={{Adapting Speaker Embeddings for Speaker Diarisation}},
  booktitle={Proc. Interspeech 2021},