Consonant characterization using correlation fractal dimension for speech recognition

This paper presents a new and promising approach in characterizing speech consonants using a fractal model for speech recognition systems. Characterization of consonants has been a difficult problem because consonant waveforms may be indistinguishable in time or frequency domain. The approach views consonant waveforms as coming from a turbulent constriction (excitation) in a human speech production system, and thus exhibiting turbulent and noise like time domain appearance. However, it departs from the usual approach by modeling consonant excitation using chaotic dynamical systems capable of generating turbulent and noise-like excitations. The scheme employs correlation fractal dimension and Takens embedding theorem to measure fractal dimension from time-series observation of the dynamical systems. It uses linear predictive coding (LPC) excitation of twenty-two consonant waveforms as the time series. Furthermore, the correlation fractal dimension is calculated using a fast Grassberger algorithm. A preliminary observation shows encouraging results because every consonant results in a unique trend of fractal dimensions for different embedding dimensions and scales.