Method for generating a speech recognition model for a non-vocabulary utterance

Abstract

The method disclosed herein facilitates the generation of a recognition model for a non-standard word uttered by a user in the context of a large vocabulary speech recognition system in which standard vocabulary models are represented by sequences of probability distributions for various acoustic symbols. Along with the probability distributions, a corresponding plurality of converse probability functions are precalculated which represent the likelihood that a particular probability distribution would correspond to a given input acoustic symbol. For a non-standard word uttered, a corresponding sequence of acoustic symbols is generated and, for each such symbol in the sequence, the most likely probability distribution is selected using the converse probability functions. For successive symbols in the utterance, a corresponding sequence of custom converse probability functions are generated, each of which is a composite of weighted contributions from the corresponding precalculated converse probability function and the converse probability functions corresponding to time-adjacent symbols in the utterance. The resulting sequence of custom converse probability functions identify a corresponding sequence of probability distributions which constitute a model of the word uttered, which model incorporates contextual information from the utterance.