A Hierarchical Stochastic Model for Automatic Prediction of Prosodic Boundary Location

Prosodic phrase structure provides important information for the understanding and naturalness of synthetic speech, and a good model of prosodic phrases has applications in both speech synthesis and speech understanding. This work describes a statistical model of an embedded hierarchy of prosodic phrase structure, motivated by results in linguistic theory. Each level of the hierarchy is modeled as a sequence of subunits at the next level, with the lowest level of the hierarchy representing factors such as syntactic branching and prosodic constituent length using a binary tree classification. A maximum likelihood solution for parameter estimation is presented, allowing automatic training of different speaking styles. For predicting prosodic phrase breaks from text, a dynamic programming algorithm is given for finding the maximum probability prosodic parse. Experimental results on a corpus of radio news demonstrate a high rate of success for predicting major and minor phrase boundaries from text without syntactic information (81% correct prediction with 4% false prediction).