FluentTTS: Text-dependent Fine-grained Style Control for Multi-style TTS

In this paper, we propose a method to flexibly control the local prosodic variation of a neural text-to-speech (TTS) model. To provide expressiveness for synthesized speech, conventional TTS models utilize utterance-wise global style embeddings that are obtained by compressing frame-level embeddings along the time axis. However, since utterance-wise global features do not contain sufficient information to represent the characteristics of word-level local features, they are not appropriate for direct use on controlling prosody at a fine scale. In multi-style TTS models, it is very important to have the capability to control local prosody because it plays a key role in finding the most appropriate text-to-speech pair among many one-to-many mapping candidates. To explicitly present local prosodic characteristics to the contextual information of the corresponding input text, we propose a module to predict the fundamental frequency (F0) of each text by conditioning on the utterance-wise global style embedding. We also estimate multi-style embeddings using a multi-style encoder, which takes as inputs both a global utterance-wise embedding and a local F0 embedding. Our multi-style embedding enhances the naturalness and expressiveness of synthesized speech and is able to control prosody styles at the word-level or phoneme-level.