Relevant pitch features selection for voice disorders families classification

Identification of voice disorders plays a major role in our life nowadays. In this context, voice analysis can be used, as a complementary technique with other traditional invasive methods, such as laryngoscopy, to identify voice disorders. This paper explores a set of acoustic features extracted from the vocal folds signals namely the pitch, to this purpose. First, 49 pitch-based features are extracted from speech recordings. Then, relevant ones are selected according to their discriminatory power between normal and pathological voice classes. To do so, different feature selection techniques have been used and compared. Afterward, KNN, SVM, Random Tree and Naive Bayes classifiers are applied to decide on the existence of a voice disorder or not and which pathological class is detected. The experimental results denote the usefulness of retained features and despite, the simplicity of classification technique (KNN for instance), the best performance in term of accuracy rate reached 91.5%.