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LF model based glottal source parameter estimation by extended Kalman filtering

Li, Haoxuan, Scaife, Ronan and O'Brien, Darragh (2011) LF model based glottal source parameter estimation by extended Kalman filtering. In: Irish Signals and Systems Conference (ISSC 2011), 23-24 June 2011, Dublin, Ireland.

Abstract
A new algorithm for glottal source parameter estimation of voiced speech based on the Liljencrants-Fant (LF) model is presented in this work. Each pitch period of the inverse filtered glottal flow derivative is divided into two phases according to the glottal closing instant and an extended Kalman filter is iteratively applied to estimate the shape controlling parameters for both phases. By searching the minimal mean square error between the reconstructed LF pulse and the original signal, an optimal set of estimates can be obtained. Preliminary experimental results show that the proposed algorithm is effective for a wide range of LF parameters for different voice qualities with different noise levels, and accuracy especially for estimation of return phase parameters compares better than standard time-domain fitting methods while requiring a significantly lower computational load.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:LF model; glottal source parameterisation; extended Kalman filter
Subjects:Computer Science > Machine learning
Engineering > Signal processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > Research Institute for Networks and Communications Engineering (RINCE)
Published in: Proceedings of the Irish Signals and Systems Conference (ISSC 2011). IET Conference Publications 566. Semantic Scholar.
Publisher:Semantic Scholar
Official URL:https://www.semanticscholar.org/paper/LF-model-bas...
Copyright Information:© 2011 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:25789
Deposited On:22 Apr 2021 15:59 by Darragh O'brien . Last Modified 22 Apr 2021 16:02
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