Summary of the NOTSOFAR-1 challenge: Highlights and learnings

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Computer Speech and Language Pub Date : 2025-03-16 DOI:10.1016/j.csl.2025.101796

Igor Abramovski , Alon Vinnikov , Shalev Shaer , Naoyuki Kanda , Xiaofei Wang , Amir Ivry , Eyal Krupka

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引用次数: 0

Abstract

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.

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NOTSOFAR-1挑战总结：亮点和经验教训

第一个远场录音设置（NOTSOFAR-1）挑战是一个关键的倡议，通过提供比以前可用的数据集更能代表现实世界商业应用程序需求的数据集，设定了新的基准。该挑战提供了30个不同环境中315次记录会议的独特组合，捕获了真实的声学条件和会话动态，以及1000小时的模拟训练数据集，该数据集具有增强的真实性，可用于现实世界的推广，包含15,000个真实的声学传递函数。在本文中，我们概述了提交挑战的系统，并分析了表现最好的方法，并假设了它们成功背后的因素。此外，我们还强调了参与者尚未探索的有前途的方向。通过提出关键发现和可操作的见解，这项工作旨在推动DASR研究和应用的进一步创新和进步。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Speech and Language 工程技术-计算机：人工智能

CiteScore

11.30

自引率

4.70%

发文量

审稿时长

22.9 weeks

期刊介绍： Computer Speech & Language publishes reports of original research related to the recognition, understanding, production, coding and mining of speech and language. The speech and language sciences have a long history, but it is only relatively recently that large-scale implementation of and experimentation with complex models of speech and language processing has become feasible. Such research is often carried out somewhat separately by practitioners of artificial intelligence, computer science, electronic engineering, information retrieval, linguistics, phonetics, or psychology.