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Updating the probabilistic density function related to an uncertain parameter of a model for producing voice, using Bayesian approach

Abstract : The aim of this paper is to use the Bayesian method for updating a probability density function (pdf) related to the tension parameter of the vocal folds. This parameter is mainly responsible for the changing of the fundamental frequency of a voice signal, generated by a mechanical/mathematical model for producing voiced sounds. Three parameters are considered uncertain in the model used: the tension parameter, the neutral glottal area and the subglottal pressure. These uncertain parameters are modeled by random variables and their prior probability density functions are constructed using the Maximum Entropy Principle. The output of the stochastic computational model is the random voice signal and the Monte Carlo method is used to solve the stochastic equations allowing realizations of the random voice signals to be generated. Experimental data are available for the fundamental frequency and the posterior probability density function of the random tension parameter is then estimated using the Bayes method.
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Contributor : Christian Soize <>
Submitted on : Thursday, September 20, 2012 - 9:10:38 PM
Last modification on : Thursday, March 19, 2020 - 11:52:03 AM
Long-term archiving on: : Friday, December 21, 2012 - 3:52:07 AM

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  • HAL Id : hal-00734169, version 1

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E. Cataldo, Christian Soize, R. Sampaio. Updating the probabilistic density function related to an uncertain parameter of a model for producing voice, using Bayesian approach. Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012), Vienna University of Technology, Sep 2012, Vienna, Austria. pp.1769-1780. ⟨hal-00734169⟩

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