  The modeling of speech can be used for speech synthesis and speech
recognition. We present a speech analysis method based on pole-zero modeling of
speech with mixed block sparse and Gaussian excitation. By using a pole-zero
model, instead of the all-pole model, a better spectral fitting can be
expected. Moreover, motivated by the block sparse glottal flow excitation
during voiced speech and the white noise excitation for unvoiced speech, we
model the excitation sequence as a combination of block sparse signals and
white noise. A variational EM (VEM) method is proposed for estimating the
posterior PDFs of the block sparse residuals and point estimates of mod- elling
parameters within a sparse Bayesian learning framework. Compared to
conventional pole-zero and all-pole based methods, experimental results show
that the proposed method has lower spectral distortion and good performance in
reconstructing of the block sparse excitation.
