A system for the recognition of spoken connected word sequences

Abstract:  Abstract Sperry Univac is developing a linguistically oriented computer system which recognizes natural spoken phrases and sentences without requiring extensive adjustments for individual users and without the need for each user to repeat every vocabulary word for system training. The recognition system produces a linguistic description of the unknown utterance by determining its component sound segments. Words from a dictionary, or lexicon, (represented in terms of the same linguistic segments) are then strung together and compared to the analysis segments to determine which syntactically allowed sequence of vocabulary words best matches the unknown utterance. Two data bases have been used recently for development and testing, including alphanumeric sequences constructed from a vocabulary of 36 words, and data management commands from a vocabulary of 64 words. For both data bases, average correct recognition scores were 95% for individual words, and 88% for complete sentences. Current plans for enhancing the recognition system include enlarging the vocabulary and number of recognizable sentence types, making additional use of prosodic information, and introducing phonetic verification rules and a word verification component. The ultimate goal of this development is to provide an effective sentence recognition system for natural and practical speech input to computers.