PUNCTUATION GENERATION INSPIRED LINGUISTIC FEATURES FOR MANDARIN PROSODIC BOUNDARY PREDICTION

Abstract

A novel statistical linguistic feature, called punctuation confidence, is proposed in this paper for assisting in prosodic break prediction in Mandarin text-to-speech. The punctuation confidence calculated from the input text is a measure of the likelihood of inserting a major PM at a word boundary. Since a punctuation in text tends to be pronounced as a break, the punctuation confidence associated with a punctuation estimate should provide useful information for break prediction from text. The idea is realized in this study by first employing a conditional random field (CRF)-based model to generate a predicted punctuation and its associated punctuation confidence for each word boundary. Then, the predicted punctuation and its punctuation confidence are combined with contextual linguistic features to predict the break type of the word boundary by an MLP (multi-layer perceptrons). Experiment on the Treebank speech corpus confirmed the effectiveness of the proposed approach.