Weighted nonlinear prediction based on Volterra series for speech analysis

The analysis of speech is usually based on linear models. In this contribution speech features are treated using nonlinear statistics of the speech signal. Therefore a nonlinear prediction based on Volterra series is applied segment-wise to the speech signal. The optimal nonlinear predictor can be determined by a vector expansion. Since the statistics of a segment is estimated a window function is integrated into the estimation procedure. Speech features are investigated representing the prediction gain between the linear and the nonlinear prediction. The analyses of speech signals show that the nonlinear features correlate with the glottal pulses. The integration of an appropriate window function into the prediction algorithm plays an important part for the results.