Utilizing Markov Chain Monte Carlo (MCMC) Method for Improved Glottal Inverse Filtering

This paper presents a new glottal inverse filtering (GIF) method that utilizes Markov chain Monte Carlo (MCMC) algorithm. First, initial estimates of the vocal tract and glottal flow are evaluated by an existing GIF method, the iterative adaptive inverse filtering (IAIF). Simultaneously, the initially estimated glottal flow is synthesized using the Klatt model and filtered with the estimated vocal tract filter. In the MCMC estimation process, the first few poles of the initial vocal tract model and the Klatt parameter are refined in order to minimize the error between the original and the synthetic signals. MCMC converges to the optimal result, and the final estimate of the vocal tract is found by averaging the parameter values of the Markov chain. Experiments show that the MCMC-based GIF method gives more accurate results compared to the original IAIF method.