An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features, linear discriminant analysis and support vector machine

Voice impairments, attention to increased unhealthy social behavior and voice abuse, have been increasing dramatically. Therefore, diagnosis of voice diseases has an important role in the opportune treatment of pathologic voices. This paper presents an extensive study in identification of different voice disorders which their origin is in the vocal folds. Firstly, a qualitative study is applied based on short-time Fourier transform (STFT) and continuous wavelet transform (CWT) in order to investigate their aptitude in the presentation of discriminative features to identify disordered voices from normal ones. Therefore, wavelet packet transform (WPT) for their ability to analyze scrutinizingly a signal at several levels of resolution is chosen as strong speech signal parameterization method. The ability of energy and entropy features, obtained from the coefficients in the output nodes of the optimum wavelet packet tree, is investigated. Linear discriminant analysis (LDA) and principal component analysis (PCA) are evaluated as feature dimension reduction methods in order to optimize recognition algorithm. The performance of each structure is evaluated in terms of the accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC). Eventually, entropy features in the sixth level of WPT decomposition along with feature dimension reduction by LDA and a support vector machine-based classification method is the most optimum algorithm that leads to the recognition rate of 100% and AUC of 100%. Proposed system clearly outperforms previous works in both respect of accuracy and reduction of residues; which may lead in full accuracy and high speed diagnosis procedure.