  The goal of expressive Text-to-speech (TTS) is to synthesize natural speech
with desired content, prosody, emotion, or timbre, in high expressiveness. Most
of previous studies attempt to generate speech from given labels of styles and
emotions, which over-simplifies the problem by classifying styles and emotions
into a fixed number of pre-defined categories. In this paper, we introduce a
new task setting, Contextual TTS (CTTS). The main idea of CTTS is that how a
person speaks depends on the particular context she is in, where the context
can typically be represented as text. Thus, in the CTTS task, we propose to
utilize such context to guide the speech synthesis process instead of relying
on explicit labels of styles and emotions. To achieve this task, we construct a
synthetic dataset and develop an effective framework. Experiments show that our
framework can generate high-quality expressive speech based on the given
context both in synthetic datasets and real-world scenarios.
