Abstract
We present a method to quantitatively characterize voice abnormalities using wavelet analysis. The proposed method uses wavelets to decompose acoustic signals, acquired clinically from patients with normal and pathological voices, into their designated frequency/time components. These components contain valuable information on the unique dynamic properties of the vocal system and correlate with specific voice conditions. A comparative analysis of these vocal signals using spectrogram is also presented. These combined analyses provide comprehensive representation and quantitative characteristics of the vocal dynamics, which may provide key indication of vocal abnormalities. Further wavelet analysis of acoustic data evaluates variations in the characteristics of the vocal signal from one glottal cycle to the next. This is valuable since vocal signals representing most pathological voice productions exhibit inter-cycle variations in intensity or/and frequency. Our results of analysis show that the wavelet analysis can be used to characterize voice pathologies and provide information regarding the type and severity of the disorder. [Work supported by NSF awarded to Yan.]
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