Estimation of a Parameter of a Non-Linear Stochastic Model for the Vocal Folds Through a Modified MCMC Algorithm

Abstract

Low order non-linear mechanical models for vocal folds, in the phonation process, have been shown to be useful in the case of normal and disordered voice studies. Despite their relative simplicity, they are able to simulate the main features of the vocal fold dynamics. A good example is the so-called two-mass Lous model, which uses few input parameters and has shown excellent results in understand-ing phonation phenomena. However, to model a real voice, it is required to infer a set of parameters of the model. Recently, some authors pointed out the advantage of using probabilistic approaches to characterize vocal fold dynamics. In this paper, a numerical stochastic model for voice production is used to simulate several vowel utterances. Then, the vocal fold tension probability density function is considered unknown and estimated from vowel utterances, using a Monte Carlo Markov Chain. Results show a good match between the estimated and actual probability densities.