Genetic Programming for detecting rhythmic stress in spoken English

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Peter Andreae and Huayang Xie and Mengjie Zhang",
  title =        "Genetic Programming for detecting rhythmic stress in
                 spoken {English}",
  journal =      "International Journal of Knowledge-Based and
                 Intelligent Engineering Systems",
  year =         "2008",
  volume =       "12",
  number =       "1",
  pages =        "15--28",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1327-2314",
  publisher =    "IOS Press",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.3233/KES-2008-12103",
  size =         "14 pages",
  abstract =     "Rhythmic stress detection is an important but
                 difficult problem in speech recognition. This paper
                 describes an approach to the automatic detection of
                 rhythmic stress in New Zealand spoken English using a
                 linear genetic programming system with speaker
                 independent prosodic features and vowel quality
                 features as terminals to classify each vowel segment as
                 stressed or unstressed. In addition to the four
                 standard arithmetic operators, this approach also uses
                 other functions such as trigonometric and conditional
                 functions in the function set to cope with the
                 complexity of the task. The error rate on the training
                 set is used as the fitness function. The approach is
                 examined and compared to a decision tree approach and a
                 support vector machine approach on a speech data set
                 with 703 vowels segmented from 60 female adult
                 utterances. The genetic programming approach achieved a
                 maximum average accuracy of 92.6percent. The results
                 suggest that the genetic programming approach developed
                 in this paper outperforms the decision tree approach
                 and the support vector machine approach for stress
                 detection on this data set in terms of the detection
                 accuracy, the ability of handling redundant features,
                 and the automatic feature selection capability.",
  notes =        "KES, see also \cite{xie:evows06}",

Genetic Programming entries for Peter Andreae Huayang Jason Xie Mengjie Zhang