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We propose a novel high-fidelity expressive speech synthesis model, UniTTS,that learns and controls overlapping style attributes avoiding interference.UniTTS represents multiple style attributes in a single unified embedding spaceby the residuals between the phoneme embeddings before and after applying theattributes. The proposed method is especially effective in controlling multipleattributes that are difficult to separate cleanly, such as speaker ID andemotion, because it minimizes redundancy when adding variance in speaker ID andemotion, and additionally, predicts duration, pitch, and energy based on thespeaker ID and emotion. In experiments, the visualization results exhibit thatthe proposed methods learned multiple attributes harmoniously in a manner thatcan be easily separated again. As well, UniTTS synthesized high-fidelity speechsignals controlling multiple style attributes. The synthesized speech samplesare presented athttps://anonymous-authors2022.github.io/paper_works/UniTTS/demos/.

UniTTS: Residual Learning of Unified Embedding Space for Speech Style Control