Evaluating the use of neural networks and acoustic measurements to identify laryngeal pathologies.

Abstract

Nineteen acoustical measurements were related to 23 larynx conditions by artificial neural networks (ANNs) and principal component analysis. An exhaustive analysis (combining all possible sets of acoustical measurements as ANN inputs) showed a performance of 99.4% for accuracy and 90.3% for sensitivity and specificity in classifying voice signals into normal and non-normal larynx conditions. In the case of individual larynx condition identification, the general sensitivity drop significantly (6.4%), although some conditions were better identified (including "vocal nodules" and "cysts") then others, reaching 99.8% of sensitivity. We also identified the acoustical measurements that produced the best classification results.