5. The computer-implemented method of claim 1 , further comprising: parameterizing each composite hidden Markov model by λ k j , where λ k j includes conventional hidden Markov model parameters {π m , T pq , b m }, π m represents prior probabilities for states of the composite hidden Markov model, T pq is a transition matrix of state transition probabilities, and b m is a parameterization of an output distribution of the composite hidden Markov model, and in which each composite hidden Markov model has a probability P(O i |λ k j ) that an i th data sequence of observations O i is generated by a j th composite hidden Markov model on a k th level of the tree.
5. The computer-implemented method of claim 1 , further comprising: parameterizing each composite hidden Markov model by λ k j , where λ k j includes conventional hidden Markov model parameters {π m , T pq , b m }, π m represents prior probabilities for states of the composite hidden Markov model, T pq is a transition matrix of state transition probabilities, and b m is a parameterization of an output distribution of the composite hidden Markov model, and in which each composite hidden Markov model has a probability P(O i |λ k j ) that an i th data sequence of observations O i is generated by a j th composite hidden Markov model on a k th level of the tree. 9. The computer-implemented method of claim 5 , in which a distance between two observations, O i and O j of the data sequences is measured by D(O i , O j )=½[p(O j |λ i )+p(O i |λ j )], where p(O j |λ i ) is a probability of generating a J th observation from an i th composite hidden Markov model, and where p(O i |λ j ) is a probability of generating an i th observation from an j th composite hidden Markov model.