  We propose an end-to-end empathetic dialogue speech synthesis (DSS) model
that considers both the linguistic and prosodic contexts of dialogue history.
Empathy is the active attempt by humans to get inside the interlocutor in
dialogue, and empathetic DSS is a technology to implement this act in spoken
dialogue systems. Our model is conditioned by the history of linguistic and
prosody features for predicting appropriate dialogue context. As such, it can
be regarded as an extension of the conventional linguistic-feature-based
dialogue history modeling. To train the empathetic DSS model effectively, we
investigate 1) a self-supervised learning model pretrained with large speech
corpora, 2) a style-guided training using a prosody embedding of the current
utterance to be predicted by the dialogue context embedding, 3) a cross-modal
attention to combine text and speech modalities, and 4) a sentence-wise
embedding to achieve fine-grained prosody modeling rather than utterance-wise
modeling. The evaluation results demonstrate that 1) simply considering
prosodic contexts of the dialogue history does not improve the quality of
speech in empathetic DSS and 2) introducing style-guided training and
sentence-wise embedding modeling achieves higher speech quality than that by
the conventional method.
