Adaptation Strategy and Clustering from Scratch for New Domains of Speaker Recognition

Pierre-Michel Bousquet, Mickaël Rouvier


This paper investigates the domain adaptation back-end methods introduced over the past years for speaker recognition, when the mismatch between training and test data induces a severe degradation of performance. This analyses lead to suggest some ways, experimentally validated, for the task of collecting in-domain data. The proposed strategy helps to quickly increase accuracy of the detection, without omitting to take into account the practical difficulties of the task of data collecting in real-life situations and without the delay for forming the expected large and speaker-labeled in-domain dataset. Moreover, a new approach of artificial speaker labeling by clustering is proposed, that dispenses of forming a preliminary annotated in-domain dataset, with a similar gain of efficiency.


 DOI: 10.21437/Odyssey.2020-12

Cite as: Bousquet, P., Rouvier, M. (2020) Adaptation Strategy and Clustering from Scratch for New Domains of Speaker Recognition. Proc. Odyssey 2020 The Speaker and Language Recognition Workshop, 81-87, DOI: 10.21437/Odyssey.2020-12.


@inproceedings{Bousquet2020,
  author={Pierre-Michel Bousquet and Mickaël Rouvier},
  title={{Adaptation Strategy and Clustering from Scratch for New Domains of Speaker Recognition}},
  year=2020,
  booktitle={Proc. Odyssey 2020 The Speaker and Language Recognition Workshop},
  pages={81--87},
  doi={10.21437/Odyssey.2020-12},
  url={http://dx.doi.org/10.21437/Odyssey.2020-12}
}