  As recent text-to-speech (TTS) systems have been rapidly improved in speech
quality and generation speed, many researchers now focus on a more challenging
issue: expressive TTS. To control speaking styles, existing expressive TTS
models use categorical style index or reference speech as style input. In this
work, we propose StyleTagging-TTS (ST-TTS), a novel expressive TTS model that
utilizes a style tag written in natural language. Using a style-tagged TTS
dataset and a pre-trained language model, we modeled the relationship between
linguistic embedding and speaking style domain, which enables our model to work
even with style tags unseen during training. As style tag is written in natural
language, it can control speaking style in a more intuitive, interpretable, and
scalable way compared with style index or reference speech. In addition, in
terms of model architecture, we propose an efficient non-autoregressive (NAR)
TTS architecture with single-stage training. The experimental result shows that
ST-TTS outperforms the existing expressive TTS model, Tacotron2-GST in speech
quality and expressiveness.
