PLAUSIBILITY FUNCTIONS IN CONTINUOUS SPEECH RECOGNITION - THE VINICS SYSTEM
We propose a new approach to phoneme-based continuous speech recognition when a time function of plausibility of observing each phoneme is given. We introduce a criterion for best sentence, related to the sum of plausibilities of individual symbols composing the sentence. Based on the idea of making use of a high plausibility region to reduce the computation load while keeping optimality, OUT method finds the most plausible sentences relating to the input speech, given the plausibility mu(a,n) of observing each phoneme a at each time, slot n. Two optimization procedures are defined to deal with the following embedded search processes: (1) find the best path connecting peaks of the plausibility functions of two successive symbols, and (2) find the best time transition slot index for two given peaks. Dynamic programming is used in these two procedures. Since the best path finding algorithm does not search slot by slot, the recognition is highly efficient. Experimental results with the VINICS system show that the method gives a better recognition precision while requiring about 1/20 computing time, compared to traditional DP based methods. The experimental system obtained a 95% sentence recognition rate on a speaker-dependent test.
plausibility functions continuous speech vinics system automatic speech recognition embedded dynamic programming acoustic symbols