Improvements on Punctuation Generation Inspired Linguistic Features for Mandarin Prosody Generation

Abstract

This paper proposes two types of machine-extracted linguistic features from unlimited text input for Mandarin prosody generation. One is the improved punctuation confidence (iPC) which is a modified version of the previously proposed punctuation confidence that represents likelihood of inserting major punctuation marks (PMs) at word boundaries. Another is the quotation confidence (QC) which measures likelihood of a word string to be quoted as a meaningful or emphasized unit. Since major PMs are highly correlated with prosodic breaks, and a quoted Chinese word string plays an important role in human language understanding, the two features potentially could provide useful information for prosody generation. The idea is realized by employing conditional random field-based models to predict major PMs, quoted word string structures, and their associated confidences, i.e. iPC and QC. Then the predicted confidences are combined with traditional linguistic features to predict prosodic-acoustic features. Both objective and subjective tests showed that the prosody generation with the proposed linguistic features performed better than the ones without the proposed features.