The occurrence of error busts is mainly observed in wireless channels. For analysis and a better understanding of such errors, in order to improve the design of communication systems, an accurate modeling of channels with this characteristic is necessary. A lot of parameter estimation methods have been studied, mainly the ones based on Hidden Markov Models (HMM). In general each method is focused in a specific communication system, on a specific layer. In this work it is proposed a new method based on a HMM with particular structure that allows the deduction of analytical expressions for all statistics of interest. The structure of the proposed model permits the generation of events that occur in a binary data sequence subject to bursts of error concerning CCITT error burst definition. The proposed model has a fixed number of only seven parameters but its number of states increase with one of those parameters that increase the accuracy but not the complexity. This work adopted techniques of optimization associated to Maximum Likelihood (ML) and Particle Swarm Optimization (PSO) to perform the parameter estimation to the proposed model. The results show that the proposed model achieves accurate characterization of channels with memory from many different sources.