NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant
  Meeting Transcription
Alon Vinnikov Amir Ivry Aviv Hurvitz Igor Abramovski Sharon Koubi Ilya Gurvich Shai Pe`er Xiong Xiao Benjamin Martinez Elizalde Naoyuki Kanda Xiaofei Wang Shalev Shaer Stav Yagev Yossi Asher Sunit Sivasankaran Yifan Gong Min Tang Huaming Wang Eyal Krupka
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.