Speaker intonation adaptation for transforming text-to-speech synthesis speaker identity

In this study, we propose a new intonation adaptation method to transform the perceived identity of a Text-To-Speech system to that of a target speaker with a small amount of training data. In the proposed method, during training we fit parametrized accent and phrase curves to parallel recordings of the target speaker F0 curves, and estimate the parameters of a mapping between the corresponding parameter spaces. During test, we fit the accent and phrase curves to the source utterances, apply the mapping, and create an F0 contour from the mapped accent and phrase curves. We compare the proposed method with a baseline adaptation method in which the source F0 contour is transformed linearly such that the per-utterance mean and variance of the target F0 contour is left unaltered. Perceptual tests showed that the proposed method was better than the baseline method in two subjective tests that assess similarity to the target speaker and speech quality, respectively.