A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition
Qiongqiong Wang, Koji Okabe, Kong Aik Lee, Takafumi Koshinaka
Abstract
This paper proposes a generalized framework for domain adaptation of Probabilistic Linear Discriminant Analysis (PLDA) in speaker recognition. It not only includes several existing supervised and unsupervised domain adaptation methods but also makes possible more flexible usage of available data in different domains. In particular, we introduce here the two new techniques described below. (1) Correlation-alignment-based interpolation and (2) covariance regularization. The proposed correlation-alignment-based-interpolation method decreases minC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">primary</sub> up to 30.5% as compared with that from an out-of-domain PLDA model before adaptation, and minC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">primary</sub> is also 5.5% lower than with a conventional linear interpolation method with optimal interpolation weights. Further, the proposed regularization technique ensures robustness in interpolations w.r.t. varying interpolation weights, which in practice is essential.
BibTeX
@inproceedings{icassp2020_ageneralizedfram,
title = {A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition},
author = {Qiongqiong Wang and Koji Okabe and Kong Aik Lee and Takafumi Koshinaka},
booktitle = {ICASSP 2020},
year = {2020}
}