Second International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2001)

Florence, Italy
September 13-15, 2001

Pathological Voice Quality Assesment Using Artificial Neural Networks

R. T. Ritchings (1), M. McGillion (1), Christopher J. Moore (2)

(1) Computer Science, School of Science, University of Salford, UK
(2) North Western Medical Physics, Christie Hospital NHS Trust, Manchester, UK

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.

Full Paper

Bibliographic reference.  Ritchings, R. T. / McGillion, M. / Moore, Christopher J. (2001): "Pathological voice quality assesment using artificial neural networks", In MAVEBA-2001, 230-234.