Modelling (sub)string-length based constraints through a grammatical inference method

In this paper a new Grammatical Inference method is proposed. In this method, a finite-state automaton is constructed by means of an incremental procedure which performs both Inference and Recognition in an integrated and simultaneous way. The (incremental) growth of the inferred automaton is controlled by explicitly minimizing (by Dynamic Programming Methods) the number of states added when each new sample is presented. This procedure has been shown to achieve an “abstraction” process which tends to capture all the relevant variability present in the local (sub)structures of the patterns being considered, and their concatenation, as well as in the lengths (extents) of these structures. The results of the application of this method to a (simple) Automatic Speech Recognition task are presented, showing its capability of achieving recognition rates which are higher than those obtained with the automata constructed by hand by experienced speech researchers.

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