We introduce the first Natural Office Talkers in Settings of Far-field Audio
Recordings (``NOTSOFAR-1'') Challenge alongside datasets and baseline system.
The challenge focuses on distant speaker diarization and automatic speech
recognition (DASR) in far-field meeting scenarios, with single-channel and
known-geometry multi-channel tracks, and serves as a launch platform for two
new datasets: First, a benchmarking dataset of 315 meetings, averaging 6
minutes each, capturing a broad spectrum of real-world acoustic conditions and
conversational dynamics. It is recorded across 30 conference rooms, featuring
4-8 attendees and a total of 35 unique speakers. Second, a 1000-hour simulated
training dataset, synthesized with enhanced authenticity for real-world
generalization, incorporating 15,000 real acoustic transfer functions. The
tasks focus on single-device DASR, where multi-channel devices always share the
same known geometry. This is aligned with common setups in actual conference
rooms, and avoids technical complexities associated with multi-device tasks. It
also allows for the development of geometry-specific solutions. The NOTSOFAR-1
Challenge aims to advance research in the field of distant conversational
speech recognition, providing key resources to unlock the potential of
data-driven methods, which we believe are currently constrained by the absence
of comprehensive high-quality training and benchmarking datasets.

引入首个远场音频记录的自然办公室对话者（“NOTSOFAR-1”）挑战，共带来数据集和基准系统。该挑战专注于远距离发言人日记和远场会议情景下的自动语音识别（DASR），并推出两个新的数据集：一是 315 个会议的基准测试数据集，每个会议平均约 6 分钟，捕捉了广泛的真实世界声学环境和对话动态；二是 1000 小时的模拟训练数据集，以增强真实世界泛化的真实性，并融合了 15000 个真实声学传递函数。该研究聚焦于单设备 DASR，在多通道设备的情况下具有相同已知的几何形状，旨在推动远距离对话语音识别领域的研究，为解锁数据驱动方法的潜力提供关键资源。