Improving robustness of speaker verification by fusion of prompted text-dependent and text-independent operation modalities

Iosif Mporas, Saeid Safavi, Reza Sotudeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationSpeech and Computer
Subtitle of host publication18th International Conference, SPECOM 2016, Proceedings
PublisherSpringer Nature Link
Pages378-385
Number of pages8
Volume9811
ISBN (Electronic)978-3-319-43958-7
ISBN (Print)9783319439570
DOIs
Publication statusPublished - 13 Aug 2016
Event18th International Conference on Speech and Computer, SPECOM 2016 - Budapest, Hungary
Duration: 23 Aug 201627 Aug 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9811
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference18th International Conference on Speech and Computer, SPECOM 2016
Country/TerritoryHungary
CityBudapest
Period23/08/1627/08/16

Keywords

  • Fusion
  • Machine learning
  • Speaker verification

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