@inproceedings{b10368f5a7a245889ce61c027e29f004,
title = "Channel adaptation of plda for text-independent speaker verification",
abstract = "Probabilistic linear discriminant analysis (PLDA) has shown to be effective for modeling channel variability in the i-vector space for text-independent speaker verification. Speaker verification is a binary hypothesis testing. Given a test segment, the verification score could be computed as the log-likelihood ratio between a speaker-adapted PLDA and the universal PLDA model. This work proposes to infer the channel factor specific to each test segment and to include the channel estimate in the PLDA models, which essentially shifts the scoring function to better match that of the test channel. We also explore the influence of covariance adaptation in both speaker and channel adaptations. Experimental results on NIST SRE'08 and SRE'10 dataset confirm that the proposed channel adaptation can be effective when the covariance is kept un-adapted, while the covariance adaptation is necessary in the speaker adaptation.",
keywords = "channel adaptation, PLDA scoring, speaker adaptation, speaker verification",
author = "Liping Chen and Lee, {Kong Aik} and Bin Ma and Wu Guo and Haizhou Li and Dai, {Li Rong}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 ; Conference date: 19-04-2014 Through 24-04-2014",
year = "2015",
month = aug,
day = "4",
doi = "10.1109/ICASSP.2015.7178973",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5251--5255",
booktitle = "2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings",
}