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Multimodal Human-Robot Interaction from the Perspective of a Speech Scientist Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Speaker Identification Using Semi-supervised Learning

Authors : Nikos Fazakis, Stamatis Karlos, Sotiris Kotsiantis, Kyriakos Sgarbas

Published in: Speech and Computer

Publisher: Springer International Publishing

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Abstract

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.

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previous chapter Sparsity Analysis and Compensation for i-Vector Based Speaker Verification
next chapter Speaker Verification Using Spectral and Durational Segmental Characteristics
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Metadata
Title
Speaker Identification Using Semi-supervised Learning
Authors
Nikos Fazakis
Stamatis Karlos
Sotiris Kotsiantis
Kyriakos Sgarbas
Copyright Year
2015
Book
Speech and Computer

Print ISBN: 978-3-319-23131-0

Electronic ISBN: 978-3-319-23132-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-23132-7_48

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