Text dependent speaker verification using binary classifiers

This paper describes how a speaker verification task can be advantageously decomposed into a series of binary classification problems, i.e. each problem discriminating between two classes only. Each binary classifier is specific to one speaker, one anti-speaker and one word. Decision trees dealing w...

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Bibliographic Details
Main Authors: Genoud, D., Moreira, M., Mayoraz, E.
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Banking
Classification tree analysis
Decision trees
Hidden Markov models
Speech recognition
Telephony
Testing
Utility programs
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Summary:This paper describes how a speaker verification task can be advantageously decomposed into a series of binary classification problems, i.e. each problem discriminating between two classes only. Each binary classifier is specific to one speaker, one anti-speaker and one word. Decision trees dealing with attributes of continuous values are used as classifiers. The set of classifiers is then pruned to eliminate the less relevant ones. Diverse pruning methods are experimented, and it is shown that when the speaker verification decision is performed with an a priori threshold, some of them give better results than a reference HMM system.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.1998.674384