Probabilistic fusion of sorted score sequences for robust speaker verification

M.C. Cheung, Man Wai Mak, S.Y. Kung

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic research

Abstract

Fusion techniques have been widely used in multi-modal biometric authentication systems. While these techniques are mainly applied to combine the outputs of modality-dependent classifiers, they can also be applied to fuse the decisions or scores from a single modality. The idea is to consider the multiple samples extracted from a single modality as independent but coming from the same source. In this chapter, we propose a single-source, multi-sample data-dependent fusion algorithm for speaker verification. The algorithm is data-dependent in that the fusion weights are dependent on the verification scores and the prior score statistics of claimed speakers and background speakers. To obtain the best out of the speaker’s scores, scores from multiple utterances are sorted before they are probabilistically combined. Evaluations based on 150 speakers from a GSM-transcoded corpus are presented. Results show that data-dependent fusion of speaker’s scores is significantly better than the conventional score averaging approach. It was also found that the proposed fusion algorithm can be further enhanced by sorting the score sequences before they are probabilistically combined.
Original languageEnglish
Title of host publicationIntelligent multimedia processing with soft computing
PublisherSpringer
Pages369-81
Number of pages287
ISBN (Print)354023053X
DOIs
Publication statusPublished - 2005

Publication series

NameStudies in fuzziness and soft computing
Volume168

Cite this