  Expressive text-to-speech has shown improved performance in recent years.
However, the style control of synthetic speech is often restricted to discrete
emotion categories and requires training data recorded by the target speaker in
the target style. In many practical situations, users may not have reference
speech recorded in target emotion but still be interested in controlling speech
style just by typing text description of desired emotional style. In this
paper, we propose a text-based interface for emotional style control and
cross-speaker style transfer in multi-speaker TTS. We propose the bi-modal
style encoder which models the semantic relationship between text description
embedding and speech style embedding with a pretrained language model. To
further improve cross-speaker style transfer on disjoint, multi-style datasets,
we propose the novel style loss. The experimental results show that our model
can generate high-quality expressive speech even in unseen style.
