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This paper suggests a way to investigate pathological voice signals from nonlinear time series analysis for clinical applications. Primarily, self similar characteristics of vocal signals have been obtained by means of a discrete wavelet analysis. Moreover, the approximate entropy of the signals has been calculated as tools for classification. Furthermore, fuzzy c-means clustering has been employed for voice signal classification. Fuzzy membership function has been proposed as a way of quantifying the amount of disorder. The results show that proposed feature vector and classification method are reliable for voice signal analysis and disorder measurement.

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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.

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