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Improving GMM–UBM speaker verification using discriminative feedback adaptation
- Source :
-
Computer Speech & Language . Jul2009, Vol. 23 Issue 3, p376-388. 13p. - Publication Year :
- 2009
-
Abstract
- Abstract: The Gaussian mixture model – Universal background model (GMM–UBM) system is one of the predominant approaches for text-independent speaker verification, because both the target speaker model and the impostor model (UBM) have generalization ability to handle “unseen” acoustic patterns. However, since GMM–UBM uses a common anti-model, namely UBM, for all target speakers, it tends to be weak in rejecting impostors’ voices that are similar to the target speaker’s voice. To overcome this limitation, we propose a discriminative feedback adaptation (DFA) framework that reinforces the discriminability between the target speaker model and the anti-model, while preserving the generalization ability of the GMM–UBM approach. This is achieved by adapting the UBM to a target speaker dependent anti-model based on a minimum verification squared-error criterion, rather than estimating the model from scratch by applying the conventional discriminative training schemes. The results of experiments conducted on the NIST2001-SRE database show that DFA substantially improves the performance of the conventional GMM–UBM approach. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 08852308
- Volume :
- 23
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Computer Speech & Language
- Publication Type :
- Academic Journal
- Accession number :
- 37149289
- Full Text :
- https://doi.org/10.1016/j.csl.2009.01.002