Signal Processing
A unifying framework for linear estimation: Generalized partitioned algorithms
Information Sciences: an International Journal
A Maximum Likelihood Approach to Continuous Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We develop a recursive maximum a posteriori classification algorithm for discrete valued stochastic processes modelled by Hidden Markov Models. The classification algorithm solves recursively the following problem: given a collection of HMM's (P^@q, Q^@q), @q @? @?, and a sequence of observations y"1, ..., y"t from a stochastic process {Y"t}"t"="1^~, find the HMM that has maximum posterior probability of producing y"1,..., y"t. This algorithm is a modification (for discrete valued stochastic processes) of the Lainiotis partition algorithm [1,2]. We prove that, subject to ergodicity and positivity assumptions on {Y"t}"t"=" "1^~, our algorithm will converge to the ''right'' (in the cross entropy sense) HMM as t - ~, for almost all sequences y"1, y"2,.... Finally, we give an example of the application of our algorithm to the classification of speech signals.