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

## TL;DR

This paper introduces a novel pole-zero speech modeling approach using a variational EM algorithm to better capture spectral features and excitation characteristics, improving speech analysis accuracy.

## Contribution

It proposes a combined block sparse and Gaussian excitation model with a variational EM method for enhanced speech spectral fitting and excitation reconstruction.

## Key findings

- Lower spectral distortion compared to traditional methods
- Effective reconstruction of block sparse excitation
- Improved speech spectral modeling accuracy

## 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 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.