Improving Robustness of Speaker Verification by Fusion of Prompted Text-Dependent and Text-Independent Operation Modalities
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In this paper we present a fusion methodology for combining prompted text-dependent and text-independent speaker verification operation modalities. The fusion is performed in score level extracted from GMM-UBM single mode speaker verification engines using several machine learning algorithms for classification. In order to improve the performance we apply clustering of the score-based data before the classification stage. The experimental results indicated that the fusion of the two operation modes improves the speaker verification performance both in terms of sensitivity and specificity by approximately 2 % and 1.5 % respectively.
KeywordsSpeaker verification Fusion Machine learning
This work was partially supported by the H2020 OCTAVE Project entitled “Objective Control for TAlker VErification” funded by the EC with Grand Agreement number 647850. The authors would like to thank Dr Md Sahidullah, Dr Nicholas Evans and Dr Tomi Kinnunen for their support in this work.
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