Fast stochastic parser for determining phrase boundaries for text-to-speech synthesis

Abstract

A stochastic parser is described which creates a phrase structure for a tagged sentence on the basis of statistical information inferred from a manually-bracketed training corpus. The information employed consists of measured probabilities for tag unigrams, symbol bigrams, bracket enclosures, bracket opening and closing, and length distribution. For experimental purposes a trees-search algorithm is used to find the highest-scoring bracketing, and a tree metric is used to measure the accuracy of the results for a test corpus. Finally, a fast algorithm for implementation is based on a finite-state approximation to the tree-search algorithm. Using these procedures, a gross level of syntactic structure is found quickly, with the main aim being that of pause insertion in real-time text-to-speech systems.