Chapter

Principles of Data Mining and Knowledge Discovery

Volume 2431 of the series Lecture Notes in Computer Science pp 373-384

Date:

SVM Classification Using Sequences of Phonemes and Syllables

  • Gerhard PaaßAffiliated withFraunhofer Institute for Autonomous Intelligent Systems (AIS)
  • , Edda LeopoldAffiliated withFraunhofer Institute for Autonomous Intelligent Systems (AIS)
  • , Martha LarsonAffiliated withFraunhofer Institute for Media Communication (IMK)
  • , Jörg KindermannAffiliated withFraunhofer Institute for Autonomous Intelligent Systems (AIS)
  • , Stefan EickelerAffiliated withFraunhofer Institute for Media Communication (IMK)

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Abstract

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.