4. In a speech recognition apparatus wherein speech units are each characterized by a sequence of template patterns, and having means for processing a speech input signal for repetitively deriving therefrom, at a frame repetition rate, a plurality of speech recognition acoustic parameters, and means responsive to said acoustic parameters for generating likelihood costs between said acoustic parameters and said speech template patterns, and for processing said likelihood costs for determining the speech units in said speech input signal, a method for inhibiting a response to nonvocabulary utterances in a speech input for which template patterns have not been created, comprising the steps of repeatedly, at a frame repetition rate, generating acoustic parameters representng said speech input, generating likelihood costs at each frame time for said acoutic parameters and said template patterns, said template patterns including a pattern representing silence, employing dynamic programming and a grammar graph for determining in response to said likelihood costs whether there has been a nonvocabulary utterance, said grammar graph having a normal speech recognition branch and a non-speech recognition branch, said non-speech recognition branch corresponding to nonvocabulary utterances for which said template patterns have not been created, and said employing step determining and selecting, using said dynamic programming, the better scoring of said speech recognition and said non-speech recognition branches.
5. The method of claim 4 further comprising the steps of assigning a fixed predetermined threshold score to an entrance arc of said non-speech recognition branch of said grammar, assigning a second fixed predetermined threshold score to a self loop are of said non-recognition branch for providing a path to remain in said non-recognition branch, and providing a silence arc leading from said self loop arc to the beginning of said entrance arc.