DuplexChat：大规模构建用于口语对话语言建模的分离说话者全双工对话语音
DuplexChat: Constructing Speaker-Separated Full-Duplex Dialogue Speech at Scale for Spoken Dialogue Language Modeling
AI总结：
针对现有大规模公共语音语料库不适用于全双工口语对话模型训练的问题，提出DuplexChat及DuplexChat-Pipe，通过特定流程构建分离说话者全双工对话语音，产出多语言语料库。
摘要：
全双工口语对话模型在每个说话者表示为单独流的对话语音上进行训练，但现有的大规模公共语音语料库大多是单声道的，不适合SDLM训练。我们提出了DuplexChat，一个用于全双工口语对话模型的开源语料库，以及DuplexChat-Pipe，一个从公共播客源构建分离说话者全双工对话语音的管道。DuplexChat-Pipe过滤特定语言的播客源，检索和清理剧集音频，提取基于分帧的双说话者对话片段，并应用语音分离和恢复以每个说话者生成一个声道。运行此管道会产生一个分离说话者的口语对话语料库，涵盖282,634小时的英语和132,723小时的日语。对DuplexChat的分析结果表明，它包含人类对话中存在的轮流对话动态。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
