Fuzzy logic based classification and assessment of pathological voice signals

In this paper an efficient fuzzy wavelet packet (WP) based feature extraction method and fuzzy logic based disorder assessment technique were used to investigate voice signals of patients suffering from unilateral vocal fold paralysis (UVFP). Mother wavelet function of tenth order Daubechies (d10) was employed to decompose signals in 5 levels. Next, WP coefficients were used to measure energy and Shannon entropy features at different spectral sub-bands. Consequently, using fuzzy c-means method, signals were clustered into 2 classes. The amount of fuzzy membership of pathological and normal signals in their corresponding clusters was considered as a measure to quantify the discrimination ability of features. A classification accuracy of 100 percent was achieved using an artificial neural network classifier. Finally, fuzzy c-means clustering method was used as a way of voice pathology assessment. Accordingly, fuzzy membership function based health index is proposed.