A new approach for speech modeling based on model reduction

Using model reduction, a new approach for low-order speech modeling is presented. In this approach, the modeling process starts with a relatively high-order (full-order) autoregressive AR model obtained by some classical methods. The AR model is then reduced using the state projection method, operat...

Full description

Saved in:
Bibliographic Details
Main Authors: Mitiche, L., Adamou-Mitiche, A.B.H.
Format: Conference Proceeding
Language:English
Subjects:
Control system synthesis
Frequency
Laboratories
Mathematical model
Reduced order systems
Signal synthesis
Signal to noise ratio
Speech synthesis
Stability
State-space methods
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Using model reduction, a new approach for low-order speech modeling is presented. In this approach, the modeling process starts with a relatively high-order (full-order) autoregressive AR model obtained by some classical methods. The AR model is then reduced using the state projection method, operating in the state space. The model reduction yields a reduced-order autoregressive moving-average (ARMA) model that interestingly preserves the key properties of the original full-order model such as stability. Line spectral frequencies (LSF) and signal-to-noise ratio (SNR) behavior are also investigated. To illustrate the performance and the effectiveness of the proposed approach, some computer simulations are conducted on some practical speech segments.
DOI:10.1109/ISCCSP.2004.1296466