TY - JOUR
T1 - Summary of the NOTSOFAR-1 challenge
T2 - Highlights and learnings
AU - Abramovski, Igor
AU - Vinnikov, Alon
AU - Shaer, Shalev
AU - Kanda, Naoyuki
AU - Wang, Xiaofei
AU - Ivry, Amir
AU - Krupka, Eyal
N1 - Publisher Copyright:
© 2025
PY - 2025/8
Y1 - 2025/8
N2 - The first Natural Office Talkers in Settings of Far-field Audio Recordings (NOTSOFAR-1) Challenge is a pivotal initiative that sets new benchmarks by offering datasets more representative of the needs of real-world business applications than those previously available. The challenge provides a unique combination of 315 recorded meetings across 30 diverse environments, capturing real-world acoustic conditions and conversational dynamics, and a 1000-hour simulated training dataset, synthesized with enhanced authenticity for real-world generalization, incorporating 15,000 real acoustic transfer functions. In this paper, we provide an overview of the systems submitted to the challenge and analyze the top-performing approaches, hypothesizing the factors behind their success. Additionally, we highlight promising directions left unexplored by participants. By presenting key findings and actionable insights, this work aims to drive further innovation and progress in DASR research and applications.
AB - The first Natural Office Talkers in Settings of Far-field Audio Recordings (NOTSOFAR-1) Challenge is a pivotal initiative that sets new benchmarks by offering datasets more representative of the needs of real-world business applications than those previously available. The challenge provides a unique combination of 315 recorded meetings across 30 diverse environments, capturing real-world acoustic conditions and conversational dynamics, and a 1000-hour simulated training dataset, synthesized with enhanced authenticity for real-world generalization, incorporating 15,000 real acoustic transfer functions. In this paper, we provide an overview of the systems submitted to the challenge and analyze the top-performing approaches, hypothesizing the factors behind their success. Additionally, we highlight promising directions left unexplored by participants. By presenting key findings and actionable insights, this work aims to drive further innovation and progress in DASR research and applications.
KW - Multi-channel speech processing
KW - Speaker diarization
KW - Speech recognition
KW - Speech separation
UR - http://www.scopus.com/inward/record.url?scp=105001126058&partnerID=8YFLogxK
U2 - 10.1016/j.csl.2025.101796
DO - 10.1016/j.csl.2025.101796
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AN - SCOPUS:105001126058
SN - 0885-2308
VL - 93
JO - Computer Speech and Language
JF - Computer Speech and Language
M1 - 101796
ER -