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.
Cite as: Bousquet, P.-M., Rouvier, M. (2020) Adaptation Strategy and Clustering from Scratch for New Domains of Speaker Recognition. Proc. The Speaker and Language Recognition Workshop (Odyssey 2020), 81-87, doi: 10.21437/Odyssey.2020-12
@inproceedings{bousquet20_odyssey,
author={Pierre-Michel Bousquet and Mickaël Rouvier},
title={{Adaptation Strategy and Clustering from Scratch for New Domains of Speaker Recognition}},
year=2020,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2020)},
pages={81--87},
doi={10.21437/Odyssey.2020-12}
}