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SVMSVM: support vector machine speaker verification methodology

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

Original languageEnglish
Title of host publicationAcoustics, Speech, and Signal Processing, 2003. Proceedings. 2003 IEEE International Conference on
Subtitle of host publication(ICASSP '03)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages221-224
Volume2
ISBN (Print)0-7803-7663-3
DOIs
Publication statusPublished - 2003
Event2003 IEEE International Conference on Acoustics, Speech, and Signal Processing - Hong Kong Exhibition and Convention Centre, Hong Kong, Hong Kong
Duration: 6 Apr 200310 Apr 2003

Conference

Conference2003 IEEE International Conference on Acoustics, Speech, and Signal Processing
CountryHong Kong
CityHong Kong
Period6/04/0310/04/03

Abstract

Support vector machines with the Fisher and score-space kernels are used for text independent speaker verification to provide direct discrimination between complete utterances. This is unlike approaches such as discriminatively trained Gaussian mixture models or other discriminative classifiers that discriminate at the frame-level only. Using the sequence-level discrimination approach we are able to achieve error-rates that are significantly better than the current state-of-the-art on the PolyVar database.

Published in:

Event

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing

6/04/0310/04/03

Hong Kong, Hong Kong

Event: Conference

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