Glottal flow derivative modeling with the wavelet smoothed excitation

This paper discusses a method for estimating glottal flow derivative model parameters using wavelet-smoothed excitation. The excitation is first estimated using the weighted recursive least squares with variable forgetting factor algorithm. The raw excitation is then smoothed by applying a discrete wavelet transform (DWT) using biorthogonal quadrature filters, and a thresholding operation carried out on the DWT amplitude coefficients, followed by an inverse DWT. The pitch period and the instant of glottal closure (IGC) are estimated from the wavelet-smoothed excitation. A six-parameter glottal flow derivative model consisting of three amplitude and three timing parameters is aligned with the IGC and optimized by minimum square error fitting to the speech waveform. The optimization is done by the method of simulated annealing. The. model is then used to reestimate the vocal-tract filter parameters in an ARX procedure followed by further stages of voice source-vocal tract estimation. The results of analysis of speech utterances from the BK TIMIT database are presented