ISCA Archive MAVEBA 2005
ISCA Archive MAVEBA 2005

Intelligent voice quality assessment posttreatment using genetic programming

Walaa Sheta, Tim Ritchings, Carl Berry

Objective techniques for assessing and classifying voice quality for patients recovering from treatment for cancer of the larynx have largely focussed on they use of Artificial Neural Networks (ANN). The results of a preliminary study are reported, where a Genetic Programming (GP) has been trained to classify recovered (normal) and abnormal voices in acoustic data, and produced much more accurate results than an ANN. In addition, the GP is able to provide impact factors for the various voice parameters, and suggests that only 6 of the 22 short-term and longterm parameters used in the current ANN studies are contributing significantly to the classifications.

Index Terms. Voice quality, classification, Artificial Neural Network, Genetic Algorithms, acoustic signals.


Cite as: Sheta, W., Ritchings, T., Berry, C. (2005) Intelligent voice quality assessment posttreatment using genetic programming. Proc. Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2005), 23-26

@inproceedings{sheta05_maveba,
  author={Walaa Sheta and Tim Ritchings and Carl Berry},
  title={{Intelligent voice quality assessment posttreatment using genetic programming}},
  year=2005,
  booktitle={Proc. Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2005)},
  pages={23--26}
}