In this paper we propose to estimate the parameters of the probabilistic linear discriminant analysis (PLDA) in text-independent i-vector speaker verification framework using a nonparametric form rather than maximum likelihood estimation (MLE) obtained by an EM algorithm. In this approach the between-speaker covariance matrix that represents global information about the speaker variability is replaced with a local estimation computed on a nearest neighbor basis for each target speaker. The nonparametric between- and within-speaker scatter matrices can better exploit the discriminant information in training data and is more adapted to sample distribution especially when it does not satisfy Gaussian assumption as in i-vectors without length-normalization. We evaluated this approach on the recent NIST 2016 speaker recognition evaluation (SRE) as well as NIST 2010 core condition and found significant performance improvement compared with a generatively trained PLDA model.
Cite as: Khosravani, A., Homayounpour, M.M. (2017) Nonparametrically Trained Probabilistic Linear Discriminant Analysis for i-Vector Speaker Verification. Proc. Interspeech 2017, 1019-1023, doi: 10.21437/Interspeech.2017-829
@inproceedings{khosravani17_interspeech,
author={Abbas Khosravani and Mohammad Mehdi Homayounpour},
title={{Nonparametrically Trained Probabilistic Linear Discriminant Analysis for i-Vector Speaker Verification}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={1019--1023},
doi={10.21437/Interspeech.2017-829}
}