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A principal problem in speech recognition is distinguishing between words and phrases that sound similar but have different meanings. Speech recognition programs produce a list of weighted candidate hypotheses for a given audio segment, and choose the "best" candidate. If the choice is incorrect, the user must invoke a correction interface that displays a list of the hypotheses and choose the desired one. The correction interface is time-consuming, and accounts for much of the frustration of today's dictation systems. Conventional dictation systems prioritize hypotheses based on language models derived from statistical techniques such as n-grams and Hidden Markov Models.We propose a supplementary method for ordering hypotheses based on Commonsense Knowledge. We filter acoustical and word-frequency hypotheses by testing their plausibility with a semantic network derived from 700,000 statements about everyday life. This often filters out possibilities that "don't make sense" from the user's viewpoint, and leads to improved recognition. Reducing the hypothesis space in this way also makes possible streamlined correction interfaces that improve the overall throughput of dictation systems.
speech recognition recognition sound mean program audio segmentation correctness user interfaces display account systems language model statistics hidden markov model method order knowledge filtering test semantic network space throughput predictive interfaces open mind common sense timing