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Given a word whose presence has been hypothesized in an unknown utterance, one way of enhancing the confidence in that hypothesis is to generate a synthetic parameterization of the word and then match it against the equivalent parametric representation of the unknown utterance. We have implemented such an approach in the speech understanding system under development at Bolt Beranek and Newman, Inc. Given a word, a synthesis-by-rule program generates a representation in terms of linear prediction spectra, which are matched against similar spectra of the raw signal using a 13-pole linear prediction error metric in conjunction with a dynamic programming time-normalization algorithm. Some automatic talker normalization procedures have been implemented in the synthesis strategy. The performance of the verification component has been measured by obtaining the distribution of verification scores for all word hypotheses generated by the speech understanding system, and determining the scores for words that should be verified correctly versus those scores for false word hypotheses. [Supported by ARPA under Contract No. N00014-75-C-0053.]

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