This paper describes a prototype system for the objective assessment of voice quality in patients recovering from various stages of laryngeal cancer. A large database of male subjects steadily phonating the vowel /i/ was used in the study, and the quality of their voices were independently assessed by a speech and language therapist (SALT) according to their 7-point ranking of subjective voice quality. The system extracts salient short-term and long-term time-domain and frequency-domain parameters from impedance (EGG) signals and these are used to train and test an Artificial Neural Network (ANN). Multi-layer Perceptron (MLP) ANNs were investigated using various combinations of these parameters, and the best results were obtained using a combination of short-term and longterm parameters, for which an accuracy of 92% was achieved. It is envisaged that this system could be used as a screening tool and provide a valuable aid to the SALT during clinical evaluation of voice quality.
Cite as: Ritchings, R.T., McGillion, M., Moore, C.J. (2001) Pathological voice quality assesment using artificial neural networks. Proc. Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2001), 230-234
@inproceedings{ritchings01_maveba,
author={R. T. Ritchings and M. McGillion and Christopher J. Moore},
title={{Pathological voice quality assesment using artificial neural networks}},
year=2001,
booktitle={Proc. Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2001)},
pages={230--234}
}