  This paper proposes an effective emotional text-to-speech (TTS) system with a
pre-trained language model (LM)-based emotion prediction method. Unlike
conventional systems that require auxiliary inputs such as manually defined
emotion classes, our system directly estimates emotion-related attributes from
the input text. Specifically, we utilize generative pre-trained transformer
(GPT)-3 to jointly predict both an emotion class and its strength in
representing emotions coarse and fine properties, respectively. Then, these
attributes are combined in the emotional embedding space and used as
conditional features of the TTS model for generating output speech signals.
Consequently, the proposed system can produce emotional speech only from text
without any auxiliary inputs. Furthermore, because the GPT-3 enables to capture
emotional context among the consecutive sentences, the proposed method can
effectively handle the paragraph-level generation of emotional speech.
