Hybridized estimations of support vector machine free parameters C and γ using a fuzzy learning strategy for microphone array-based speaker recognition in a Kinect sensor-deployed environment

Hybridized estimations of support vector machine free parameters C and γ using a fuzzy learning... The support vector machine (SVM) is a popular classification model for speaker verification. However, although SVM is suitable for classifying speakers, the uncertain values of the free parameters C and γ of the SVM model have been a challenging technique problem. An improper value set provided for the free parameter pair (C, γ) can cause dissatisfactory performance in the recognition accuracy of speaker verification. Moreover, the sound source localization information of the collected acoustic data has a large effect on the recognition performance of SVM speaker verification. In response, this study developed a sound source localization-driven fuzzy scheme to help determine the optimal value set of (C, γ) for the establishment of an SVM model. Specifically, this scheme adopts the estimated information of time difference of arrival (TDOA) derived from the Kinect microphone array (containing both the angle and distance information of the acoustic data of the speaker), to optimally calculate the value set of the SVM free parameters C and γ. It was demonstrated that speaker verification using the SVM with a properly estimated parameter pair (C, γ) is more accurate than that with only an arbitrarily given value set for the parameter pair (C, γ) on recognition rate. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Hybridized estimations of support vector machine free parameters C and γ using a fuzzy learning strategy for microphone array-based speaker recognition in a Kinect sensor-deployed environment

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-4499-y
Publisher site
See Article on Publisher Site

Abstract

The support vector machine (SVM) is a popular classification model for speaker verification. However, although SVM is suitable for classifying speakers, the uncertain values of the free parameters C and γ of the SVM model have been a challenging technique problem. An improper value set provided for the free parameter pair (C, γ) can cause dissatisfactory performance in the recognition accuracy of speaker verification. Moreover, the sound source localization information of the collected acoustic data has a large effect on the recognition performance of SVM speaker verification. In response, this study developed a sound source localization-driven fuzzy scheme to help determine the optimal value set of (C, γ) for the establishment of an SVM model. Specifically, this scheme adopts the estimated information of time difference of arrival (TDOA) derived from the Kinect microphone array (containing both the angle and distance information of the acoustic data of the speaker), to optimally calculate the value set of the SVM free parameters C and γ. It was demonstrated that speaker verification using the SVM with a properly estimated parameter pair (C, γ) is more accurate than that with only an arbitrarily given value set for the parameter pair (C, γ) on recognition rate.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Mar 1, 2017

References

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