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.
Ritchings
R T
RT
Department of Computer Science, University of Salford, Salford, UK. t.ritchings@salford.ac.uk
McGillion
M
M
Moore
C J
CJ
D000158:Acoustic Impedance Tests / Q000379:methods; D005260:Female; D006801:Humans; D007822:Laryngeal Neoplasms / Q000150:complications; D008297:Male; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013061:Speech Acoustics; D013064:Speech Disorders / Q000145:classification* / Q000209:etiology / Q000503:physiopathology*; D013068:Speech Production Measurement / Q000379:methods*; D014833:Voice Quality