Multi-class SVM optimization using MCE training with application to topic identification

This paper presents a minimum classification error (MCE) training approach for improving the accuracy of multi-class support vector machine (SVM) classifiers. We have applied this approach to topic identification (topic ID) for human-human telephone conversations from the Fisher corpus using ASR lat...

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Bibliographic Details
Published in2010 IEEE International Conference on Acoustics, Speech and Signal Processing pp. 5350 - 5353
Main Author Hazen, T J
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2010
Subjects
Automatic speech recognition
Calibration
Kernel
Laboratories
Lattices
MCE training
Natural languages
Support vector machine classification
Support vector machines
SVM classifiers
Telephony
topic identification
Virtual manufacturing
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Summary:This paper presents a minimum classification error (MCE) training approach for improving the accuracy of multi-class support vector machine (SVM) classifiers. We have applied this approach to topic identification (topic ID) for human-human telephone conversations from the Fisher corpus using ASR lattice output. The new approach yields improved performance over the traditional techniques for training multi-class SVM classifiers on this task.
ISBN:9781424442959
1424442958
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2010.5494948