Hierarchical Prosody Modeling of English Speech and its Application to TTS

Abstract

In this paper, a hierarchical prosody modeling approach for English speech is proposed. It is an extended version of the HPM approach proposed previously for Mandarin speech. It first designs a syllable-based, statistical prosodic model to describe various relationships of prosodic-acoustic features of the speech signal, linguistic features of the associated text, and prosodic tags representing the underlining prosody structure of the speech. It then employs a prosody labeling and modeling algorithm to estimate the model parameters and label the prosodic tags of all training utterances simultaneously from a prosody-unlabeled speech corpus. Experimental results on a corpus containing many paragraphic utterances of a female English-majored Chinese speaker show that the inferred parameters of the model are all meaningful. We then use the trained model to generate prosodic information for a TTS system. An informal listening test shows that the synthetic speech sounds quite natural.