Genetic Programming for Automatic Stress Detection in Spoken English
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @TechReport{vuw-CS-TR-06-4,
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author = "Huayang Xie and Mengjie Zhang and Peter Andreae",
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title = "Genetic Programming for Automatic Stress Detection in
Spoken English",
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institution = "Computer Science, Victoria University of Wellington",
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year = "2006",
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number = "CS-TR-06-4",
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address = "New Zealand",
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keywords = "genetic algorithms, genetic programming, Speech
recognition, stress detection, decision trees, support
vector machines",
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URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-4.abs.html",
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URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-4.pdf",
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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