Comparing ANNs and Genetic Programming for Voice Quality Assessment Post-Treatment
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{journals/aai/RitchingsBS08,
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title = "Comparing {ANNs} and Genetic Programming for Voice
Quality Assessment Post-Treatment",
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author = "Tim Ritchings and Carl Berry and Walaa Sheta",
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journal = "Applied Artificial Intelligence",
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year = "2008",
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number = "3",
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volume = "22",
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bibdate = "2008-12-11",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/aai/aai22.html#RitchingsBS08",
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pages = "198--207",
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DOI = "doi:10.1080/08839510701734343",
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keywords = "genetic algorithms, genetic programming",
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abstract = "In the U.K., the rehabilitation of a patient's voice
following treatment for cancer of the larynx is managed
by Speech and Language Therapists (SALT), who listen to
a patient's stylized speech and then use their
experience and domain knowledge to make an assessment
of the current quality of the patient's voice. This
process is very subjective and time consuming, and
could benefit from using AI techniques to provide
objective, reproducible assessments of voice quality. A
comparative study of voice quality assessment
post-treatment using Artificial Neural Networks (ANN),
the preferred AI technique in this application area,
and Genetic Programming (GP) is described, using the
same dataset, training, and verification procedures.
The GP approach was found to give more accurate
classifications of bad quality (immediately
post-treatment) and good quality (recovered) voicings
than the ANN, and in addition, gave indication of the
most significant parameters in the input dataset.",
- }
Genetic Programming entries for
Tim Ritchings
Carl Berry
Walaa Sheta
Citations