Automatic Detection Of Vocal Fold Paralysis and Edema

In this paper we propose a combined scheme of linear prediction analysis for feature extraction along with linear projection methods for feature reduction followed by known pattern recognition methods on the purpose of discriminating between normal and pathological voice samples. Two different cases of speech under vocal fold pathology are examined: vocal fold paralysis and vocal fold edema. Three known classifiers are tested and compared in both cases, namely the Fisher linear discriminant, the #-nearest neighbor classifier, and the nearest mean classifier. The performance of each classifier is evaluated in terms of the probabilities of false alarm and detection or the receiver operating characteristic. The datasets used are part of a database of disordered speech developed by Massachusetts Eye and Ear Infirmary. The experimental results indicate that vocal fold paralysis and edema can easily be detected by any of the aforementioned classifiers