Joint source-filter modeling using flexible basis functions

Improving on recent work on joint source-filter analysis of speech waveforms, we explore improvements to an autoregressive model with exogenous inputs represented by flexible basis functions. Following a brief review of the maximum likelihood estimators of the model parameters, the Cramer-Rao bounds are derived to provide evidence for the challenging nature of estimating source and filter characteristics with overlapping spectra. Wavelet expansion of the exogenous inputs is employed, and the selection of an appropriate subset of wavelets is described as an online, signal-adaptive approach. Results from synthesized and real vowel analysis illustrate the promise of iterative wavelet shrinkage using soft and hard thresholding and an alternative regularization method.