A comparison of waveform fractal dimension techniques for voice pathology classification

In this paper, an attempt is made to compare and analyze the various waveform fractal dimension techniques for voice pathology classification. Three methods of estimating the fractal dimension directly from the time-domain waveform have been compared. The methods used are Katz algorithm, Higuchi algorithm and the Hurst exponent calculated using the rescaled range (R/S) analysis. Furthermore, the effects of the window size, the base waveform used and score-level fusion with Mel frequency cepstral coefficients (MFCC) has also been evaluated. The features have been extracted from two different base waveforms, the speech signal and the Teager energy operator (TEO) phase of the speech signal. Experiments have been carried out on a subset of the Massachusetts Eye and Ear Infirmary (MEEI) database and classifier used is a 2^nd order polynomial classifier. A classification accuracy of 97.54 %was achieved on score-level fusion, an increase in performance by about 2 % as compared to MFCC alone.