Improving out-domain PLDA speaker verification using unsupervised inter-dataset variability compensation approach

Ahilan Kanagasundaram; David Dean; Sridha Sridharan

ICASSP 2015accepted0 citations

Improving out-domain PLDA speaker verification using unsupervised inter-dataset variability compensation approach

Ahilan Kanagasundaram, David Dean, Sridha Sridharan

Abstract

Experimental studies have found that when the state-of-the-art probabilistic linear discriminant analysis (PLDA) speaker verification systems are trained using out-domain data, it significantly affects speaker verification performance due to the mismatch between development data and evaluation data. To overcome this problem we propose a novel unsupervised inter dataset variability (IDV) compensation approach to compensate the dataset mismatch. IDV-compensated PLDA system achieves over 10% relative improvement in EER values over out-domain PLDA system by effectively compensating the mismatch between in-domain and out-domain data.

BibTeX

@inproceedings{icassp2015_improvingoutdoma,
  title = {Improving out-domain PLDA speaker verification using unsupervised inter-dataset variability compensation approach},
  author = {Ahilan Kanagasundaram and David Dean and Sridha Sridharan},
  booktitle = {ICASSP 2015},
  year = {2015}
}