Fast sequential closed-phase glottal inverse filtering based on optimally weighted recursive least squares
Tat Chiu Luk, J.R. Deller · The Journal of the Acoustical Society of America · 1985
Presented in this paper is the theoretical basis, with simulation verification, for a sequential method of deconvolution of the glottal waveform from voiced speech. The technique is based upon a linear predictive model of the vocal tract, and the assumption of a “pseudo-closed phase” (PCP) (noisy closed phase) of the glottis during each pitch period. Existing techniques for closed phase glottal inverse filtering (CPIF) employ “batch”-type methods which are generally slow, highly user interactive, and restricted to the use of one cycle of data in the analysis. The basic ideas underlying CPIF, and a brief review of existing methods, will be presented in the first part of the paper. In the second part of this paper, requisite theoretical results and the new method will be developed. In particular, these will include a unified theory of CPIF in which the selection of closed phase points is viewed as a data weighting process. This viewpoint readily admits the use of more than one cycle of data in the analysis (advantageous when the data fare noisy), and further leads to the use of optimal weighting of the accepted data. The theory of “membership set” identification is used as a basis for optimization of weights. Novel weighting strategies employed in a conventional recursive least-squares algorithm form the basis of the improved technique. The last part of the paper contains simulation studies and computational considerations. The new method is shown to result in significant increases in both accuracy and computational efficiency.