mlsanityData to 14 Oct 2026

ChatGPT-EDSS: Empathetic Dialogue Speech Synthesis Trained from ChatGPT-derived Context Word Embeddings

arXiv 2305.13724 ↗First posted 23 May 2023cs.SD cs.CL cs.LGcited by 4

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Abstract (arXiv)

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/.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 23 May 2023.

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18 of 33 references.

References · 18

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6 more
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Citing papers per year: 2024: 3; 2025: 1.

4 papers.

  1. 2024Talk With Human-like Agents: Empathetic Dialogue Through Perceptible Acoustic Reception and Reactioncited by 13
  2. 2024Recent Advancement of Emotion Cognition in Large Language Modelscited by 9
  3. 2025DialogueAgents: A Hybrid Agent-Based Speech Synthesis Framework for Multi-Party Dialoguecited by 6
  4. 2024Llama-VITS: Enhancing TTS Synthesis with Semantic Awarenesscited by 1