Genetic Programming for Automatic Stress Detection in Spoken English

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Huayang Xie and Mengjie Zhang and Peter Andreae",
  title =        "Genetic Programming for Automatic Stress Detection in
                 Spoken English",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoWorkshops2006: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
                 {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}",
  year =         "2006",
  month =        "10-12 " # apr,
  editor =       "Franz Rothlauf and Jurgen Branke and 
                 Stefano Cagnoni and Ernesto Costa and Carlos Cotta and 
                 Rolf Drechsler and Evelyne Lutton and Penousal Machado and 
                 Jason H. Moore and Juan Romero and George D. Smith and 
                 Giovanni Squillero and Hideyuki Takagi",
  series =       "LNCS",
  volume =       "3907",
  publisher =    "Springer Verlag",
  address =      "Budapest",
  publisher_address = "Berlin",
  ISBN =         "3-540-33237-5",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "460--471",
  URL =          "",
  DOI =          "doi:10.1007/11732242_41",
  abstract =     "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.",
  notes =        "part of \cite{evows06} also known as
                 \cite{conf/evoW/XieZA06} See also

Genetic Programming entries for Huayang Jason Xie Mengjie Zhang Peter Andreae