International Journal of Speech Technology

, Volume 3, Issue 3–4, pp 263–276 | Cite as

A Comparative Study of Several Feature Transformation and Learning Methods for Phoneme Classification

  • András Kocsor
  • László Tóth
  • András Kuba
  • Kornél Kovács
  • Márk Jelasity
  • Tibor Gyimóthy
  • János Csirik

Abstract

This paper examines the applicability of some learning techniques for speech recognition, more precisely, for the classification of phonemes represented by a particular segment model. The methods compared were the IB1 algorithm (TiMBL), ID3 tree learning (C4.5), oblique tree learning (OC1), artificial neural nets (ANN), and Gaussian mixture modeling (GMM), and, as a reference, a hidden Markov model (HMM) recognizer was also trained on the same corpus. Before feeding them into the learners, the segmental features were additionally transformed using either linear discriminant analysis (LDA), principal component analysis (PCA), or independent component analysis (ICA). Each learner was tested with each transformation in order to find the best combination. Furthermore, we experimented with several feature sets, such as filter-bank energies, mel-frequency cepstral coefficients (MFCC), and gravity centers. We found LDA helped all the learners, in several cases quite considerably. PCA was beneficial only for some of the algorithms, and ICA improved the results quite rarely and was bad for certain learning methods. From the learning viewpoint, ANN was the most effective and attained the same results independently of the transformation applied. GMM behaved worse, which shows the advantages of discriminative over generative learning. TiMBL produced reasonable results; C4.5 and OC1 could not compete, no matter what transformation was tried.

speech and phoneme recognition feature space transformations discriminative and generative learning 

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Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • András Kocsor
    • 1
  • László Tóth
    • 1
  • András Kuba
    • 1
  • Kornél Kovács
    • 1
  • Márk Jelasity
    • 1
  • Tibor Gyimóthy
    • 1
  • János Csirik
    • 1
  1. 1.Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and of the University of SzegedSzegedHungary

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