An Evaluation of One-Class Classification Techniques for Speaker Verification

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Title: An Evaluation of One-Class Classification Techniques for Speaker Verification
Authors: Brew, AnthonyGrimaldi, MarcoCunningham, Pádraig
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Date: 13-Aug-2007
Online since: 2021-07-29T16:21:47Z
Abstract: Speaker verification is a challenging problem in speaker recognition where the objective is to determine whether a segment of speech in fact comes from a specific individual. In supervised machine learning terms this is a challenging problem as, while examples belonging to the target class are easy to gather, the set of counterexamples is completely open. In this paper we cast this as a one-class classification problem and evaluate a variety of state-of-the-art one-class classification techniques on a benchmark speech recognition dataset. We show that of the one-class classification techniques, Gaussian Mixture Models shows the best performance on this task.
Type of material: Technical Report
Publisher: University College Dublin. School of Computer Science and Informatics
Series/Report no.: UCD CSI Technical Reports; UCD-CSI-2007-8
Copyright (published version): 2007 the Authors
Keywords: Speaker recognitionMachine learningOne-class classification
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Language: en
Status of Item: Not peer reviewed
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Appears in Collections:Computer Science and Informatics Technical Reports

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