Comparative Study: HMM and SVM for Automatic Articulatory Feature Extraction

  • Supphanat Kanokphara
  • Jan Macek
  • Julie Carson-Berndsen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


Generally speech recognition systems make use of acoustic features as a representation of speech for further processing. These acoustic features are usually based on human auditory perception or signal processing. More recently, Articulatory Feature (AF) based speech representations have been investigated by a number of speech technology researchers. Articulatory features are motivated by linguistic knowledge and hence may better represent speech characteristics. In this paper, we introduce two popular classification models, Hidden Markov Model (HMM) and Support Vector Machine (SVM), for automatic articulatory feature extraction. HMM-based systems are found to be best when there is good balance in the numbers of positive and negative examples in the data while SVM is better in the unbalanced data condition.


Support Vector Machine Hide Markov Model Speech Recognition Speech Signal Speech Recognition System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Supphanat Kanokphara
    • 1
  • Jan Macek
    • 1
  • Julie Carson-Berndsen
    • 1
  1. 1.UCD DublinUCD School of Computer Science and InformaticsDublinIreland

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