Genetic Programming for Automatic Stress Detection in Spoken English

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@TechReport{vuw-CS-TR-06-4,

• author = "Huayang Xie and Mengjie Zhang and Peter Andreae",
• title = "Genetic Programming for Automatic Stress Detection in Spoken English",
• institution = "Computer Science, Victoria University of Wellington",
• year = "2006",
• number = "CS-TR-06-4",
• address = "New Zealand",
• keywords = "genetic algorithms, genetic programming, Speech recognition, stress detection, decision trees, support vector machines",
• URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-4.abs.html",
• URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-4.pdf",
• abstract = "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 92.61per cent 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.",

}

Genetic Programming entries for Huayang Jason Xie Mengjie Zhang Peter Andreae

Citations