Prediction of abstract prosodic labels for speech synthesis

Abstract

Higher quality speech synthesis is required to make text-to-speech technologies useful in more applications, and prosody is one component of synthesis technology with the greatest need for improvement. This paper describes computational models for the prediction of abstract prosodic labels for synthesis—accent location, symbolic tones and relative prominence level—from text that is tagged with part-of-speech labels and marked for prosodic constituent structure. Specifically, the model uses multiple levels of a prosodic hierarchy and at each level combines decision tree probability functions with Markov sequence assumptions. An advantage of decision trees is the ability to incorporate linguistic knowledge in an automatic training framework, which is needed for building systems that reflect particular speaking styles. Studies of accent and tone variability across speakers are reported and used to motivate new evaluation metrics. Prediction experiments show an improvement in accuracy of prominence location prediction over simple decision trees, with accuracy similar to the level of variability observed across speakers.