Speaker identification using nonlinear dynamical features

This paper reports results obtained in a speaker identification system which combines commonly used feature parameters, such as LP-derived cepstral coefficients and pitch, with nonlinear dynamic features, namely, fractal dimension, entropy and largest Lyapunov exponent, extracted from every window of 30 ms of speech, applied every 10 ms. The corpus used in the tests is composed of 37 different speakers, and the best results are obtained when the nonlinear dynamic features are included, suggesting that the information added with these features was not present in the others so far.