Nonlinear dynamic analysis in signal typing of pathological human voices
A new quantitative scheme used in signal typing of pathological human voices is presented, which is based on nonlinear dynamic analysis. The conducted research shows that correlation dimension analysis reveals significant differences among various types of voice signals: nearly periodic type 1 signals, type 2 signals containing bifurcations or modulations, and aperiodic type 3 signals. The correlation dimensions of each signal type statistically increase from type 1 to type 3 signals. This study suggests that nonlinear dynamic analysis represents a valuable new method to quantitatively classify pathological human voice signals.
