caa a22 4500 463207068 CHVBK 20180405153129.0 cr unu---uuuuu 170326e20070401xx s 000 0 eng 10.1007/s10462-008-9071-8 doi (NATIONALLICENCE)springer-10.1007/s10462-008-9071-8 An evaluation of one-class classification techniques for speaker verification [Elektronische Daten] [Anthony Brew, Marco Grimaldi, Pádraig Cunningham] 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 counter-examples is completely open. This makes it difficult to cast this as a supervised classification problem as it is difficult to construct a representative set of counter examples. So 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 construct this as a two-level classification process whereby, at the lower level, speech segments of 20ms in length are classified and then a decision on an complete speech sample is made by aggregating these component classifications. We show that of the one-class classification techniques we evaluate, Gaussian Mixture Models shows the best performance on this task. Springer Science+Business Media B.V., 2008 One-class classifiers nationallicence Speaker verification nationallicence Gaussian mixture models nationallicence Brew Anthony Department of Computer Science and Informatics, University College Dublin, Dublin, Ireland aut Grimaldi Marco Department of Computer Science and Informatics, University College Dublin, Dublin, Ireland aut Cunningham Pádraig Department of Computer Science and Informatics, University College Dublin, Dublin, Ireland aut Artificial Intelligence Review Springer Netherlands 27/4(2007-04-01), 295-307 0269-2821 27:4<295 2007 27 10462 https://doi.org/10.1007/s10462-008-9071-8 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10462-008-9071-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Brew Anthony Department of Computer Science and Informatics, University College Dublin, Dublin, Ireland aut NATIONALLICENCE 700 1- Grimaldi Marco Department of Computer Science and Informatics, University College Dublin, Dublin, Ireland aut NATIONALLICENCE 700 1- Cunningham Pádraig Department of Computer Science and Informatics, University College Dublin, Dublin, Ireland aut NATIONALLICENCE 773 0- Artificial Intelligence Review Springer Netherlands 27/4(2007-04-01), 295-307 0269-2821 27:4<295 2007 27 10462 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer