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Selective Fusion for Speaker Verification in Surveillance.
- Source :
- Intelligence & Security Informatics; 2005, p269-279, 11p
- Publication Year :
- 2005
-
Abstract
- This paper presents an improved speaker verification technique that is especially appropriate for surveillance scenarios. The main idea is a meta-learning scheme aimed at improving fusion of low- and high-level speech information. While some existing systems fuse several classifier outputs, the proposed method uses a selective fusion scheme that takes into account conveying channel, speaking style and speaker stress as estimated on the test utterance. Moreover, we show that simultaneously employing multi-resolution versions of regular classifiers boosts fusion performance. The proposed selective fusion method aided by multi-resolution classifiers decreases error rate by 30% over ordinary fusion. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540259992
- Database :
- Supplemental Index
- Journal :
- Intelligence & Security Informatics
- Publication Type :
- Book
- Accession number :
- 32913926
- Full Text :
- https://doi.org/10.1007/11427995_22