Intelligent classification of electrolaryngograph signals
R. T. Ritchings, M. McGillion, Christopher J. Moore · 2005
This paper describes a prototype system for the intelligent classification of electrolaryngograph (EGG) signals in order to provide an objective assessment of voice quality in patients at different stages of recovery after treatment for larynx cancer. The system extracts salient short-term and longterm time-domain and frequency-domain parameters from EGG signals taken from male patients steadily phonating the vowel /i/. The quality of these voices was also independently assessed by a speech and language therapist (SALT) according to their 7-point ranking of subjective voice quality. These data were used to train and test a multilayer perceptron (MLP) neural network to classify EGG signals in terms of voice quality. Several MLP configurations were investigated using various combinations of these signal parameters, and the best results were obtained using a combination of short-term and long-term parameters for which an accuracy of 92% was achieved. It is envisaged that this system could be used as a valuable aid to the SALT during clinical evaluation of voice quality.