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

2026-07-06

AI Summary

This work addresses the scarcity of high-quality, speaker-separated full-duplex conversational speech data—a critical bottleneck in training spoken dialogue language models—given that most existing large-scale public speech corpora are monaural and lack explicit speaker turn structure. To bridge this gap, the authors introduce the DuplexChat project, which presents the first large-scale effort to construct speaker-separated, full-duplex conversational datasets from massive monaural podcast archives. They develop DuplexChat-Pipe, a comprehensive pipeline integrating language filtering, audio cleaning, diarization-guided two-speaker segment extraction, and speech separation with restoration. The resulting corpus comprises 282,634 hours of English and 132,723 hours of Japanese conversational speech, faithfully preserving natural turn-taking dynamics and substantially advancing resource availability for spoken dialogue research.

Abstract

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.

Problem

• full-duplex
• spoken dialogue
• speaker separation
• speech corpus
• language modeling

Innovation

• full-duplex dialogue
• speaker separation
• spoken dialogue language modeling
• speech corpus construction
• diarization-guided extraction