A Data-Driven Framework for Intonational Phrase Break Prediction

For the present work, we attempt to study the issue of automatic acquisition of intonational phrase breaks. A mathematically well-formed framework is suggested, which is based on Bayesian theory. Based on two different assumptions regarding the conditional independence of the input attributes, we have come up with two Bayesian implementations, namely the Naïve Bayes and the Bayesian Networks classifiers. As a performance benchmark, we evaluated the experimental result against CART, an acclaimed algorithm in the field of intonational phrase break detection that has demonstrated stat-of-the-art figures. Our approach utilizes minimal morphological and syntactic resources in a finite length window, i.e. the POS label and the type of syntactic phrase boundary, a novel attribute that has not been applied to the specific task before. On a 5500 word training set, the Bayesian networks approach proved to be the most effective, depicting precision and recall figures in the range of 82% and 77% respectively.