Robust local scoring function for text-independent speaker verification

Ming Liu, Thomas S Huang, Zhengyou Zhang

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

Abstract

Traditionally, the Universal Background Model (UBM) is viewed as the background model of the entire acoustic feature space. We propose a novel interpretation of the UBM model, and consider it as a mapping function that transforms the variable length observations (speech utterances) into a fixed dimensional feature vector (sufficient statistics). After this mapping, a similarity measurement is computed on the fixed dimensional features. With this novel interpretation, we proposed a new similarity measurement which produces more than 10% relative improvement over the conventional UBM-MAP framework in both equal error rate and detection cost function.

Original languageEnglish (US)
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages1146-1149
Number of pages4
DOIs
StatePublished - Dec 1 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: Aug 20 2006Aug 24 2006

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
CityHong Kong
Period8/20/068/24/06

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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