SVM Classification Using Sequences of Phonemes and Syllables
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In this paper we use SVMs to classify spoken and written documents. We show that classification accuracy for written material is improved by the utilization of strings of sub-word units with dramatic gains for small topic categories. The classification of spoken documents for large categories using sub-word units is only slightly worse than for written material, with a larger drop for small topicc ategories. Finally it is possible, without loss, to train SVMs on syllables generated from written material and use them to classify audio documents. Our results confirm the strong promise that SVMs hold for robust audio document classification, and suggest that SVMs can compensate for speech recognition error to an extent that allows a significant degree of topic independence to be introduced into the system.
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- SVM Classification Using Sequences of Phonemes and Syllables
- Book Title
- Principles of Data Mining and Knowledge Discovery
- Book Subtitle
- 6th European Conference, PKDD 2002 Helsinki, Finland, August 19–23, 2002 Proceedings
- pp 373-384
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series Subtitle
- Lecture Notes in Artificial Intelligence
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 3. Department of Computer Science, University of Helsinki
- Author Affiliations
- 4. Fraunhofer Institute for Autonomous Intelligent Systems (AIS), 53754, St. Augustin, Germany
- 5. Fraunhofer Institute for Media Communication (IMK), 53754, St. Augustin, Germany
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