关注差距：合成对话数据对多说话人ASR和说话人识别的影响
Mind the Gap: Impact of Synthetic Conversational Data on Multi-Talker ASR and Speaker Diarization
AI总结：
研究探讨合成数据对多说话人ASR和说话人识别的影响，发现任务依赖性显著，增加语音重叠提升ASR但损害识别，广泛来源多样性优于精确领域匹配，合成数据训练优于真实数据基线。
摘要：
近年来，多说话人ASR（MT-ASR）和说话人识别（SD）的突破依赖于合成数据以缓解大规模对话录音的稀缺性，但特定模拟选择的影响尚不明确。为了弥合模拟混合物与现实世界互动之间的差距，我们对领先的MT-ASR（DiCoW）和SD（Sortformer）系统进行了合成数据生成研究。通过引入高效的开源模拟器FastMSS，我们分析了轮替动态、源领域、语音增强和数据混合策略。我们的发现表明，最优的模拟配方高度依赖于任务：增加语音重叠有利于ASR但损害识别。此外，广泛来源多样性始终优于精确领域匹配。最终，仅合成数据训练的方法优于真实数据基线，结合模拟数据和真实录音在两项任务中均取得显著提升。
英文摘要：
Recent breakthroughs in multi-talker ASR (MT-ASR) and speaker diarization (SD) rely on synthetic data to mitigate the scarcity of large-scale conversational recordings, yet the impact of specific simulation choices remains poorly understood. To mind the gap between simulated mixtures and real-world interactions, we present a study of synthetic data generation for leading MT-ASR (DiCoW) and SD (Sortformer) systems. By introducing FastMSS, a highly efficient open-source simulator, we analyze turn-taking dynamics, source domain, acoustic augmentation, and data mixing strategies. Our findings reveal that optimal simulation recipes are highly task-dependent: increasing speech overlap benefits ASR but degrades diarization. Furthermore, broad source diversity consistently outperforms exact domain matching. Ultimately, synthetic-only training approaches real-data baselines, and combining simulated data with real recordings yields substantial gains over real-only training across both tasks.
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。
