Abstract
Recently, a model identification system (MIS) has been studied for ARMA parameter estimation. Since in MIS an estimator with a high-order ARMA parameter and a model reduction algorithm are used, the tracking ability of the spectral estimation is good. However in real speech analysis, estimation errors are caused by the use of a low-pass filter for A/D conversion. In order to compensate for this estimation error we propose a weighted MIS (WMIS) which includes a compensation for the characteristics of the low-pass filter. The WMIS estimates the input of the reference model and time-varying ARMA parameters, and achieves the property of rapid convergence by using a highorder model. Furthermore the algorithm for minimum realization is shown as a model reduction algorithm. The proposed algorithms are applied to synthetic speech and real speech, and it is shown that the estimated spectra sufficiently represent the variation of formants without jitters in the high-frequency part.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
