  State-of-the-art Text-To-Speech (TTS) models are capable of producing
high-quality speech. The generated speech, however, is usually neutral in
emotional expression, whereas very often one would want fine-grained emotional
control of words or phonemes. Although still challenging, the first TTS models
have been recently proposed that are able to control voice by manually
assigning emotion intensity. Unfortunately, due to the neglect of intra-class
distance, the intensity differences are often unrecognizable. In this paper, we
propose a fine-grained controllable emotional TTS, that considers both inter-
and intra-class distances and be able to synthesize speech with recognizable
intensity difference. Our subjective and objective experiments demonstrate that
our model exceeds two state-of-the-art controllable TTS models for
controllability, emotion expressiveness and naturalness.
