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Prosodic patterns provide important cues for resolving syntactic ambiguity, and might be used to improve the accuracy of automatic speech understanding. With this goal, we propose a method of scoring syntactic parses in terms of observed prosodic cues, which can be used in ranking sentence hypotheses and associated parses. Specifically, the score is the probability of acoustic features of a hypothesized word sequence given an associated syntactic parse, based on acoustic and "language" (prosody/syntax) models that represent probabilities in terms of abstract prosodic labels. This work reports initial efforts aimed at extending the algorithm to spontaneous speech, specifically the ATIS task, where the prosody/parse score is shown to improve the average rank of the correct sentence hypothesis.

Prosody/parse scoring and its application in ATIS