Genetic Programming for Automatic Stress Detection in Spoken English

  • Huayang Xie
  • Mengjie Zhang
  • Peter Andreae
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


This paper describes an approach to the use of genetic programming (GP) for the automatic detection of rhythmic stress in spoken New Zealand English. A linear-structured GP system uses speaker independent prosodic features and vowel quality features as terminals to classify each vowel segment as stressed or unstressed. Error rate is used as the fitness function. In addition to the standard four arithmetic operators, this approach also uses several other arithmetic, trigonometric, and conditional functions in the function set. The approach is evaluated on 60 female adult utterances with 703 vowels and a maximum accuracy of 92.61% is achieved. The approach is compared with decision trees (DT) and support vector machines (SVM). The results suggest that, on our data set, GP outperforms DT and SVM for stress detection, and GP has stronger automatic feature selection capability than DT and SVM.


Support Vector Machine Prosodic Feature Lexical Stress Vowel Quality Genetic Programming 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ladefoged, P.: Three Areas of experimental phonetics. Oxford University Press, London (1967)Google Scholar
  2. 2.
    Ladefoged, P.: A Course in Phonetics, 3rd edn. Harcourt Brace Jovanovich, New York (1993)Google Scholar
  3. 3.
    Waibel, A.: Recognition of lexical stress in a continuous speech system - a pattern recognition approach. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Tokyo, Japan, pp. 2287–2290 (1986)Google Scholar
  4. 4.
    Jenkin, K.L., Scordilis, M.S.: Development and comparison of three syllable stress classifiers. In: Proceedings of the International Conference on Spoken Language Processing, Philadelphia, USA, pp. 733–736 (1996)Google Scholar
  5. 5.
    van Kuijk, D., Boves, L.: Acoustic characteristics of lexical stress in continuous speech. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Munich, Germany, vol. 3, pp. 1655–1658 (1999)Google Scholar
  6. 6.
    Xie, H., Andreae, P., Zhang, M., Warren, P.: Detecting stress in spoken English using decision trees and support vector machines. Australian Computer Science Communications (Data Mining, CRPIT 32) 26, 145–150 (2004)Google Scholar
  7. 7.
    Conrads, M., Nordin, P., Banzhaf, W.: Speech sound discrimination with genetic programming. In: Proceedings of the First European Workshop on Genetic Programming, pp. 113–129 (1998)Google Scholar
  8. 8.
    Francone, F.D.: Discipulus owner’s manual (2004)Google Scholar
  9. 9.
    Xie, H., Andreae, P., Zhang, M., Warren, P.: Learning models for English speech recognition. Australian Computer Science Communications (Computer Science, CRPIT 26) 26, 323–330 (2004)Google Scholar
  10. 10.
    Quinlan, J.: C4.5: Programs for machine learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  11. 11.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2003),
  12. 12.
    Koza, J.R.: Genetic Programming — On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  13. 13.
    Dy, J.G., Brodley, C.E.: Feature selection for unsupervised learning. Journal of Machine Learning Research 5, 845–889 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Huayang Xie
    • 1
  • Mengjie Zhang
    • 1
  • Peter Andreae
    • 1
  1. 1.School of Mathematics, Statistics and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

Personalised recommendations