An Evaluation of One-Class Classification Techniques for Speaker Verification
|Title:||An Evaluation of One-Class Classification Techniques for Speaker Verification||Authors:||Brew, Anthony; Grimaldi, Marco; Cunningham, Pádraig||Permanent link:||http://hdl.handle.net/10197/12361||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 recognition; Machine learning; One-class classification||Other versions:||https://web.archive.org/web/20080226040105/http:/csiweb.ucd.ie/Research/TechnicalReports.html||Language:||en||Status of Item:||Not peer reviewed||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science and Informatics Technical Reports|
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