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

Kanagasundaram, Ahilan, Dean, David, & Sridharan, Sridha (2015) Improving out-domain PLDA speaker verification using unsupervised inter-dataset variability compensation approach. In Proceedings of 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2015), IEEE, Brisbane, QLD, pp. 4654-4658.

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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.

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3 citations in Scopus
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ID Code: 83774
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: speaker recognition, domain adaptation, PLDA, inter-dataset variability
DOI: 10.1109/ICASSP.2015.7178853
ISBN: 9781467369978
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Institutes > Information Security Institute
Copyright Owner: Copyright 2015 IEEE
Copyright Statement: Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Deposited On: 27 Apr 2015 22:45
Last Modified: 12 Sep 2015 18:19

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