1. In a speech recognition system in which vocabulary models are represented by sequences of precalculated probability distributions for a predetermined set of acoustic symbols; the method of expeditiously generating a model for a non-vocabulary word uttered by a user, said method comprising: pre-calculating along with said probability distributions a corresponding set of converse probability functions which represent the probabilities that particular ones of said precalculated probability distributions would correspond to a given input acoustic symbol; for a non-standard word uttered by a user, generating a corresponding utterance sequence of acoustic symbols; for each symbol in said utterance sequence, selecting the respective precalculated converse probability function thereby to generate a single valued sequence of said converse probability functions; for successive symbols in said utterance sequence, generating respective custom converse probability functions each of which is a composite of weighted contributions from the precalculated converse probability function corresponding to the respective symbol and the precalculated converse probability functions corresponding to time-adjacent symbols in the utterance sequence, the weighting of the contributions being independent of the utterance sequence; and for each custom converse probability function, selecting the best matching one of said precalculated probability distributions, whereby the resulting single valued sequence of selected precalculated probability distributions provides a model of the word uttered by the user.
1. In a speech recognition system in which vocabulary models are represented by sequences of precalculated probability distributions for a predetermined set of acoustic symbols; the method of expeditiously generating a model for a non-vocabulary word uttered by a user, said method comprising: pre-calculating along with said probability distributions a corresponding set of converse probability functions which represent the probabilities that particular ones of said precalculated probability distributions would correspond to a given input acoustic symbol; for a non-standard word uttered by a user, generating a corresponding utterance sequence of acoustic symbols; for each symbol in said utterance sequence, selecting the respective precalculated converse probability function thereby to generate a single valued sequence of said converse probability functions; for successive symbols in said utterance sequence, generating respective custom converse probability functions each of which is a composite of weighted contributions from the precalculated converse probability function corresponding to the respective symbol and the precalculated converse probability functions corresponding to time-adjacent symbols in the utterance sequence, the weighting of the contributions being independent of the utterance sequence; and for each custom converse probability function, selecting the best matching one of said precalculated probability distributions, whereby the resulting single valued sequence of selected precalculated probability distributions provides a model of the word uttered by the user. 2. The method as set forth in claim 1 wherein said acoustic symbols represent spectral frames and speech input to said recognition system including said nonstandard word is periodically analyzed as to spectral content to obtain said spectral frames.