Nonlinear analysis of speech signals: generalized dimensions and lyapunov exponents

In this paper, we explore modern methods and algorithms from fractal/chaotic systems theory for modeling speech signals in a multidimensional phase space and extracting characteristic invariant measures like generalized fractal dimensions and Lyapunov exponents. Such measures can capture valuable information for the characterisation of the multidimensional phase space which is closer to the true dynamics since they are sensitive to the frequencywithwhich the attractor visits different regions and the rate of exponential divergence of nearby orbits, respectively. Further we examine the classification capability of related nonlinear features over broad phoneme classes. The results of these preliminary experiments indicate that the information carried by these novel nonlinear feature sets is important and useful.