Estimating Trajectory Hmm Parameters Using Monte Carlo Em With Gibbs Sampler

In the present paper, the Monte Carlo EM (MCEM) algorithm with a Gibbs sampler is applied for estimating parameters of a trajectory HMM, which has been derived from an HMM by imposing explicit relationships between static and dynamic features. The trajectory HMM can alleviate two limitations of the HMM, which are i) constant statistics within a state, and ii) conditional independence of state output probabilities, without increasing the number of model parameters. In a speaker-dependent continuous speech recognition experiment, trajectory HMMs estimated by the MCEM algorithm achieved significant improvements over the corresponding HMMs trained by the EM (Baum-Welch) algorithm