Let's Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation
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Abstract (arXiv)
In this paper, we introduce a novel Face-to-Face spoken dialogue model. It processes audio-visual speech from user input and generates audio-visual speech as the response, marking the initial step towards creating an avatar chatbot system without relying on intermediate text. To this end, we newly introduce MultiDialog, the first large-scale multimodal (i.e., audio and visual) spoken dialogue corpus containing 340 hours of approximately 9,000 dialogues, recorded based on the open domain dialogue dataset, TopicalChat. The MultiDialog contains parallel audio-visual recordings of conversation partners acting according to the given script with emotion annotations, which we expect to open up research opportunities in multimodal synthesis. Our Face-to-Face spoken dialogue model incorporates a textually pretrained large language model and adapts it into the audio-visual spoken dialogue domain by incorporating speech-text joint pretraining. Through extensive experiments, we validate the effectiveness of our model in facilitating a face-to-face conversation. Demo and data are available at https://multidialog.github.io and https://huggingface.co/datasets/IVLLab/MultiDialog, respectively.
Key facts. Cited by 41 papers in the map (data through 14 Oct 2026). Builds on 49 of its 81 references that are in the map. First posted on arXiv 12 Jun 2024.
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49 of 81 references.
References · 49
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- 2020A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wildcited by 672
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- 2021Towards Real-World Blind Face Restoration with Generative Facial Priorcited by 349
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- 2021Generative Spoken Language Modeling from Raw Audiocited by 340
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- 2022Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Predictioncited by 287
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- 2021YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyonecited by 278
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- 2015The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systemscited by 172
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- 2018A Dataset for Document Grounded Conversationscited by 104
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- 2023Emotional Listener Portrait: Neural Listener Head Generation with Emotioncited by 17
- 2023Textless Unit-to-Unit training for Many-to-Many Multilingual Speech-to-Speech Translationcited by 11
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Citing papers per year: 2024: 6; 2025: 17; 2026: 18.
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- 2026COT-TTS: Audio Context-Aware Text-to-Speech with Chain-of-Thought Reasoningcited by 0
- 2026COT-TTS: Audio Context-Aware Text-to-Speech with Chain-of-Thought Reasoning19 Sep 2026
- 2026EmotionDialogCN: A Spontaneous Multimodal Dataset for Mandarin Emotional Dialogue21 Aug 2026
- 2026Let Me Look at You: Advanced Facial Expression Modeling for Conversational Speech Synthesis27 Jul 2026
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- 2026StanceBench: A Benchmark for Audio LLM-Based Interpersonal Stance Evaluation from Speech27 Jun 2026
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- 2026Integrating Fine-Grained Audio-Visual Evidence for Robust Multimodal Emotion Reasoning26 Jan 2026
- 2025DyStream: Streaming Dyadic Talking Heads Generation via Flow Matching-based Autoregressive Model30 Dec 2025
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12 most cited of 31 All 31 (JSON)
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- 2026AuEmoChat: Authentic Emotion Understanding and Rendering for Conversational Speech Synthesis17 Jul 2026
- 2026StanceBench: A Benchmark for Audio LLM-Based Interpersonal Stance Evaluation from Speech27 Jun 2026
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