Speaker-independent style conversion for HMM-based expressive speech synthesis
Hiroki Kanagawa, Takashi Nose, Takao Kobayashi · 2013
This paper proposes a technique for creating target speaker's expressive-style model from the target speaker's neutral style speech in HMM-based speech synthesis. The technique is based on the style adaptation using linear transforms where speaker-independent transformation matrices are estimated in advance using pairs of neutral and target-style speech data of multiple speakers. By applying the obtained transformation matrices to a new speaker's neutral-style model, we can convert the style expressivity of the acoustic model to the target style without preparing any target-style speech of the speaker. In addition, we introduce a speaker adaptive training (SAT) framework into the transform estimation to reduce the acoustic difference among speakers. We subjectively evaluate the performance of the style conversion in terms of the naturalness, speaker similarity, and style reproducibility.