COMPARING ANNs AND GENETIC PROGRAMMING FOR VOICE QUALITY ASSESSMENT POST-TREATMENT

Authors:
Tim Ritchings;Carl Berry;Walaa Sheta
Affiliations:
School of Computing, Science and Engineering, University of Salford, Salford, U.K.;School of Computing, Science and Engineering, University of Salford, Salford, U.K.;Mubarak City for Scientific Research, Burg El-Arab, Egypt
Venue:
Applied Artificial Intelligence
Year:
2008

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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.