Abstract Chaotic characteristics in speech by calculating the maximum Lyapunov exponents of 38 Mandarin phonemes are presented. The physical significance of three nonlinear features of human speech, i.e. the largest Lyapunov exponent, the secondorder dynamical entropy, and the fractal dimension, is studied. A speaker recognition system based on the Gaussian mixture model is established. On the decision layer, the recognition results obtained from MFCC and nonlinear dynamics are combined in a serial manner to give an improved performance. The experimental result shows nonlinear dynamics coefficients can distinguish different speaker and aid speaker identification only by MFCC features.
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2006, Vol. 19 
