阿拉伯语语音识别中的方言覆盖与泛化
Dialectal Coverage And Generalization in Arabic Speech Recognition
AI总结：
本文提出一套优化的开源阿拉伯语ASR模型，涵盖MSA、多种方言及语码转换，覆盖17国数据和至少11种变体，显著提升了方言覆盖与识别性能。
摘要：
开发稳健的阿拉伯语自动语音识别（ASR）系统需要有效的策略来管理其多样性。现有的ASR系统主要覆盖现代标准阿拉伯语（MSA）变体和少数高资源方言，但在覆盖范围和对众多口语变体的泛化能力上存在不足。在阿拉伯世界的不同地区，英语和法语的语码转换也很常见，这挑战了单语阿拉伯语模型的性能。在这项工作中，我们引入了一套经过优化的ASR模型，能够有效识别多种口语阿拉伯语变体，包括MSA、各种方言和语码转换。我们提供了开源的预训练模型，覆盖来自17个阿拉伯语国家的数据；微调的MSA和方言ASR模型，包含至少11种变体；以及覆盖语码转换话语中嵌入语言的多语言ASR模型。我们评估了这些口语变体的ASR性能，并展示了与先前模型相比在覆盖范围和性能上的提升。
英文摘要：
Developing robust automatic speech recognition (ASR) systems for Arabic requires effective strategies to manage its diversity. Existing ASR systems mainly cover the modern standard Arabic (MSA) variety and few high-resource dialects, but fall short in coverage and generalization across the multitude of spoken variants. Code-switching with English and French is also common in different regions of the Arab world, which challenges the performance of monolingual Arabic models. In this work, we introduce a suite of ASR models optimized to effectively recognize multiple variants of spoken Arabic, including MSA, various dialects, and code-switching. We provide open-source pre-trained models that cover data from 17 Arabic-speaking countries, and fine-tuned MSA and dialectal ASR models that include at least 11 variants, as well as multi-lingual ASR models covering embedded languages in code-switched utterances. We evaluate ASR performance across these spoken varieties and demonstrate both coverage and performance gains compared to prior models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
