ChatGPT-EDSS: Empathetic Dialogue Speech Synthesis Trained from
  ChatGPT-derived Context Word Embeddings
Yuki Saito Shinnosuke Takamichi Eiji Iimori Kentaro Tachibana Hiroshi Saruwatari
We propose ChatGPT-EDSS, an empathetic dialogue speech synthesis (EDSS)
method using ChatGPT for extracting dialogue context. ChatGPT is a chatbot that
can deeply understand the content and purpose of an input prompt and
appropriately respond to the user's request. We focus on ChatGPT's reading
comprehension and introduce it to EDSS, a task of synthesizing speech that can
empathize with the interlocutor's emotion. Our method first gives chat history
to ChatGPT and asks it to generate three words representing the intention,
emotion, and speaking style for each line in the chat. Then, it trains an EDSS
model using the embeddings of ChatGPT-derived context words as the conditioning
features. The experimental results demonstrate that our method performs
comparably to ones using emotion labels or neural network-derived context
embeddings learned from chat histories. The collected ChatGPT-derived context
information is available at
https://sarulab-speech.github.io/demo_ChatGPT_EDSS/.