A SEGMENTAL APPROACH TO TEXT-INDEPENDENT SPEAKER VERIFICATION

J. Černocký, D. Petrovska-Delacrétaz, S. Pigeon, Patrick Verlinde, G. Chollet

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Current text-independent speaker verification systems are usually based on modeling globally the probability density function (PDF) of the speaker feature vectors. In this paper, segmental approaches to text-independent speaker verification are discussed. Unlike the schemes based on Large Vocabulary Continuous Speech Recognition (LVCSR) with previously trained phone models, our systems are based on units derived in unsupervised manner using the ALISP (Automatic Language Independent Processing) tools. Speaker modeling is then done independently for each class of speech sounds. Among the techniques to merge the class-dependent scores, linear combination was tested and logistic regression and a method based on the Mixture of Experts technique are under investigation. The experimental results were obtained on the data from the NIST-NSA'98 campaign.

Original languageEnglish
Title of host publication Proceedings of the 6th European Conference on Speech Communication and Technology, EUROSPEECH 1999
Pages2207-2210
Number of pages4
Publication statusPublished - 1999
Event6th European Conference on Speech Communication and Technology, EUROSPEECH 1999 - Budapest, Hungary
Duration: 5 Sept 19999 Sept 1999

Conference

Conference6th European Conference on Speech Communication and Technology, EUROSPEECH 1999
Country/TerritoryHungary
CityBudapest
Period5/09/999/09/99

Keywords

  • data fusion
  • segmental approach
  • text-independent speaker verification

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