A novel prosody adaptation method for Mandarin concatenation-based text-to-speech system

Abstract

The paper presents a prosody adaptation method which is able to adapt the prosody model of text to speech (TTS) to a new style with a small training corpus. Unlike the conventional prosody mapping between two parallel prosody features, the paper tries to integrate the prosody conversion into the prosody generation model of TTS. In the paper, we use a template-based prosody model which consists of two major parts: the prosody template library and the template parameter prediction trees for TTS system. With this model, the prosody adaptation is realized by the following two steps: converting the prosody template library to the target speaker’s prosody based on the mapping methods, re-training prosody prediction trees with the small target training set. In the model, some transformation algorithms, including linear regression, Gaussian Mixture Model (GMM) and Classification and Regression Tree (CART) are involved. Experimental results show that the prosody adaptation system can generate synthesized speech which is much similar with the target speaker.