Modified k-mean clustering method of HMM states for initialization of Baum-Welch training algorithm

Hidden Markov models are widely used for recognition algorithms (speech, writing, gesture, ...). In this paper, a classical set of models is considered: state space of hidden variable is discrete and observation probabilities are modeled as Gaussian distributions. The models parameters are generally estimated with training sequences and the Baum-Welch algorithm, i.e. an expectation maximization algorithm. However this kind of algorithm is well known to be sensitive to its initialization point. The problem of this initialization point choice is addressed in this paper: a model with a very large number of states which describe training sequences with accuracy is first constructed. The number of states is then reduced using a k-mean algorithm on the state. This algorithm is compared to other methods based on a k-mean algorithm on the data with numerical simulations.