A variational EM method for pole-zero modeling of speech with mixed block sparse and Gaussian excitation

Abstract

Abstract-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 modelling 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.

Autoregressive Parameter Estimation with Dnn-Based Pre-Processing

“…For voiced speech, however, the excitation signal does not resemble a white, Gaussian excitation signal as in the autoregressive process, but is much more accurately modelled by an impulse train [3]. As a consequence of this, many alternative ways of estimating the AR-parameters have been proposed based on the prior knowledge on the power spectral density (PSD) [10,11,5] or the excitation signal [3,12,13]. For example, El-Jaroudi and Makhoul proposed in [10] the discrete all-pole (DAP) approach in which the AR-parameters are estimated by minimising the Itakura-Saito (IS) divergence for a discrete set of points, leading to better performance for voiced speech.…”