发表机构
The University of British Columbia; King Fahd University of Petroleum & Minerals; Avignon Université; ELYADATA; King Abdullah University of Science & Technology; NYU Abu Dhabi（不列颠哥伦比亚大学; 法赫德国王石油与矿业大学; 阿维尼翁大学; ELYADATA公司; 阿卜杜拉国王科技大学; 纽约大学阿布扎比分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
NADI 2026作为第二届多方言阿拉伯语语音处理共享任务，涵盖五个任务八个子任务，引入TTS、SLT和SLU，强调现实评估，发现域外泛化是主要瓶颈，并验证了专用模型与集成方法的有效性。
AI 中文摘要
NADI 2026是精细阿拉伯语方言识别（NADI）共享任务系列的第七届，也是第二届专门针对多方言阿拉伯语语音处理的任务。本届包含五个任务和八个子任务，涵盖自动语音识别（ASR）、口语方言识别（SDID）、文本到语音合成（TTS）、口语语言翻译（SLT）和口语语言理解（SLU）。NADI 2026强调通过低带宽、混合方言、代码切换、域外和零样本设置进行现实评估，同时首次在该系列中引入TTS、SLT和SLU。该共享任务吸引了来自至少13个国家的21个参赛团队，提交了48份测试阶段结果和14篇系统描述论文。结果表明，域外泛化仍然是主要瓶颈，并凸显了近期阿拉伯语专用语音模型、多模态方言识别方法和集成方法的有效性。总体而言，NADI 2026为稳健的阿拉伯语方言语音处理提供了更广泛且更具挑战性的基准。
英文摘要
NADI 2026 is the seventh edition of the Nuanced Arabic Dialect Identification (NADI) shared task series and the second dedicated to multidialectal Arabic speech processing. This edition comprises five tasks and eight subtasks spanning Automatic Speech Recognition (ASR), Spoken Dialect Identification (SDID), Text-to-Speech (TTS), Spoken Language Translation (SLT), and Spoken Language Understanding (SLU). NADI 2026 emphasizes realistic evaluation through low-bandwidth, mixed-dialect, code-switched, out-of-domain, and zero-shot settings, while introducing TTS, SLT, and SLU to the series for the first time. The shared task attracted 21 participating teams from at least 13 countries, with 48 test-phase submissions and 14 submitted system-description papers. Results show that out-of-domain generalization remains a major bottleneck and highlight the effectiveness of recent Arabic-specialized speech models, multimodal dialect identification approaches, and ensemble methods. Overall, NADI 2026 provides a broader and more challenging benchmark for robust Arabic dialect speech processing.
