  Conversational Speech Synthesis (CSS) aims to accurately express an utterance
with the appropriate prosody and emotional inflection within a conversational
setting. While recognising the significance of CSS task, the prior studies have
not thoroughly investigated the emotional expressiveness problems due to the
scarcity of emotional conversational datasets and the difficulty of stateful
emotion modeling. In this paper, we propose a novel emotional CSS model, termed
ECSS, that includes two main components: 1) to enhance emotion understanding,
we introduce a heterogeneous graph-based emotional context modeling mechanism,
which takes the multi-source dialogue history as input to model the dialogue
context and learn the emotion cues from the context; 2) to achieve emotion
rendering, we employ a contrastive learning-based emotion renderer module to
infer the accurate emotion style for the target utterance. To address the issue
of data scarcity, we meticulously create emotional labels in terms of category
and intensity, and annotate additional emotional information on the existing
conversational dataset (DailyTalk). Both objective and subjective evaluations
suggest that our model outperforms the baseline models in understanding and
rendering emotions. These evaluations also underscore the importance of
comprehensive emotional annotations. Code and audio samples can be found at:
https://github.com/walker-hyf/ECSS.
