1. 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 representing said speech input, generating likelihood costs at each frame time for said acoustic parameters and said template patterns, said template patterns including a pattern representing silence, beginning a normal speech recognition process whenever said cost for an active template pattern is better than a predetermined threshold value, and reverting to a non-speech recognition process whenever said cost of said template patterns, including silence, is worse than said predetermined threshold value.
1. 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 representing said speech input, generating likelihood costs at each frame time for said acoustic parameters and said template patterns, said template patterns including a pattern representing silence, beginning a normal speech recognition process whenever said cost for an active template pattern is better than a predetermined threshold value, and reverting to a non-speech recognition process whenever said cost of said template patterns, including silence, is worse than said predetermined threshold value. 2. The method of claim 1 further comprising the steps of setting a second threshold value and remaining in said non-speech recognition process until a likelihood cost of said silence template in better than said second threshold.