mlsanityData to 13 Oct 2026

DuplexChat: Constructing Speaker-Separated Full-Duplex Dialogue Speech at Scale for Spoken Dialogue Language Modeling

arXiv 2607.04941 ↗First posted 6 Jul 2026cs.CL cs.SD eess.AScited by 3

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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

  1. 2020Scaling Laws for Neural Language Modelscited by 6,494
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  10. 2026PersonaPlex: Voice and Role Control for Full Duplex Conversational Speech Modelscited by 59
  11. 2024YODAS: Youtube-Oriented Dataset for Audio and Speechcited by 56
  12. 2026DialogueSidon: Recovering Full-Duplex Dialogue Tracks from In-the-Wild Dialogue Audiocited by 4
1 more
  1. 2024J-CHAT: Japanese Large-scale Spoken Dialogue Corpus for Spoken Dialogue Language Modelingcited by 2

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3 papers.