Speaker Identification Using Semi-supervised Learning

Semi-supervised classification methods use available unlabeled data, along with a small set of labeled examples, to increase the classification accuracy in comparison with training a supervised method using only the labeled data. In this work, a new semi-supervised method for speaker identification...

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Bibliographic Details
Published inSpeech and Computer Vol. 9319; pp. 389 - 396
Main Authors Fazakis, Nikos, Karlos, Stamatis, Kotsiantis, Sotiris, Sgarbas, Kyriakos
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2015
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Artificial intelligence
Classification using labeled
Information retrieval
Pattern recognition
Semi-supervised learning
Speaker identification
Unlabeled data
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Summary:Semi-supervised classification methods use available unlabeled data, along with a small set of labeled examples, to increase the classification accuracy in comparison with training a supervised method using only the labeled data. In this work, a new semi-supervised method for speaker identification is presented. We present a comparison with other well-known semi-supervised and supervised classification methods on benchmark datasets and verify that the presented technique exhibits better accuracy in most cases.
ISBN:3319231316
9783319231310
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-23132-7_48