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The relative size and location of prosodic phrase boundaries provides an important cue for resolving syntactic ambiguity. In previous work, we have introduced an analysis/synthesis formalism for scoring parses in terms of the similarity between prosodic patterns recognized from a given utterance and synthesized for the hypothesized parse. This paper describes a new approach to the synthesis problem, as well as an alternative scoring criterion. Specifically, a decision tree is designed to predict prosodic phrase structure for a given syntactic parse, and the tree is used to compute a parse score, which now is the probability of the recognized break sequence. Unlike the rule-based synthesis algorithm used in the previous work, the decision tree can be automatically trained and can therefore be designed specifically for different speaking styles or task domains. In experiments with a corpus of ambiguous sentences spoken by FM radio announcers, we have achieved disambiguation performance similar to the rule-based algorithm, which is close to the performance of human subjects in perceptual experiments using the scoring algorithm with hand labeled breaks.

Probabilistic parse scoring based on prosodic phrasing