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The modeling of speech can be used for speech synthesis and speechrecognition. We present a speech analysis method based on pole-zero modeling ofspeech with mixed block sparse and Gaussian excitation. By using a pole-zeromodel, instead of the all-pole model, a better spectral fitting can beexpected. Moreover, motivated by the block sparse glottal flow excitationduring voiced speech and the white noise excitation for unvoiced speech, wemodel the excitation sequence as a combination of block sparse signals andwhite noise. A variational EM (VEM) method is proposed for estimating theposterior PDFs of the block sparse residuals and point estimates of mod- ellingparameters within a sparse Bayesian learning framework. Compared toconventional pole-zero and all-pole based methods, experimental results showthat the proposed method has lower spectral distortion and good performance inreconstructing of the block sparse excitation.

A Variational EM Method for Pole-Zero Modeling of Speech with Mixed Block Sparse and Gaussian Excitation