DuplexChat: Constructing Speaker-Separated Full-Duplex Dialogue Speech at Scale for Spoken Dialogue Language Modeling
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Abstract (arXiv)
Full-duplex spoken dialogue models are trained on conversational speech in which each speaker is represented as a separate stream, but existing large-scale public speech corpora are mostly monaural, making them unsuited for SDLM training. We present DuplexChat, an open-source corpus for full-duplex spoken dialogue models, and DuplexChat-Pipe, a pipeline for constructing speaker-separated full-duplex dialogue speech from public podcast feeds. DuplexChat-Pipe filters language-specific podcast feeds, retrieves and cleans episode audio, extracts diarization-guided two-speaker dialogue clips, and applies speech separation and restoration to produce one channel per speaker. Running this pipeline yields a speaker-separated spoken dialogue corpus covering 282,634 hours of English and 132,723 hours of Japanese. Analysis results on DuplexChat show that it contains turn-taking dynamics present in human dialogues.
Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Builds on 13 of its 22 references that are in the map. First posted on arXiv 6 Jul 2026.
Builds on
13 of 22 references.
References · 13
- 2020Scaling Laws for Neural Language Modelscited by 6,494
- 2024Moshi: a speech-text foundation model for real-time dialoguecited by 550
- 2023SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilitiescited by 523
- 2021GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audiocited by 393
- 2020DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressorscited by 239
- 2024Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generationcited by 202
- 2024Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLMcited by 137
- 2022Generative Spoken Dialogue Language Modelingcited by 126
- 2023TorchAudio-Squim: Reference-less Speech Quality and Intelligibility measures in TorchAudiocited by 59
- 2026PersonaPlex: Voice and Role Control for Full Duplex Conversational Speech Modelscited by 59
- 2024YODAS: Youtube-Oriented Dataset for Audio and Speechcited by 56
- 2026DialogueSidon: Recovering Full-Duplex Dialogue Tracks from In-the-Wild Dialogue Audiocited by 4
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Citing papers per year: 2026: 3.
3 papers.
- 2026ConversationalVoice: Full-Duplex Speech Data from Real Conversations through Source-Faithful Reconstruction and Conversation-Grounded Expansioncited by 1
- 2026Learning Natural Conversational Behavior in Tandem Speech-to-Speech Models with Randomized Guidancecited by 0
- 2026Evaluating Real-Time Voice Agents: From Component Quality to Grounded Outcomescited by 0
- 2026Evaluating Real-Time Voice Agents: From Component Quality to Grounded Outcomes25 Sep 2026
- 2026Learning Natural Conversational Behavior in Tandem Speech-to-Speech Models with Randomized Guidance25 Sep 2026
- 2026ConversationalVoice: Full-Duplex Speech Data from Real Conversations through Source-Faithful Reconstruction and Conversation-Grounded Expansion8 Sep 2026