Pathological voice quality assessment using artificial neural networks.

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 was independently assessed by a speech and language therapist (SALT) according to their seven-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 long-term parameters, for which an accuracy of 92% was achieved. It is envisaged that this system could be used as an assessment tool, providing a valuable aid to the SALT during clinical evaluation of voice quality.