{"title":"Arabic Automatic Speech Recognition: Challenges and Progress","authors":"Fatma Zahra Besdouri , Inès Zribi , Lamia Hadrich Belguith","doi":"10.1016/j.specom.2024.103110","DOIUrl":null,"url":null,"abstract":"<div><p>This paper provides a structured examination of Arabic Automatic Speech Recognition (ASR), focusing on the complexity posed by the language’s diverse forms and dialectal variations. We first explore the Arabic language forms, delimiting the challenges encountered with Dialectal Arabic, including issues such as code-switching and non-standardized orthography and, thus, the scarcity of large annotated datasets. Subsequently, we delve into the landscape of Arabic resources, distinguishing between Modern Standard Arabic (MSA) and Dialectal Arabic (DA) Speech Resources and highlighting the disparities in available data between these two categories. Finally, we analyze both traditional and modern approaches in Arabic ASR, assessing their effectiveness in addressing the unique challenges inherent to the language. Through this comprehensive examination, we aim to provide insights into the current state and future directions of Arabic ASR research and development.</p></div>","PeriodicalId":49485,"journal":{"name":"Speech Communication","volume":"163 ","pages":"Article 103110"},"PeriodicalIF":2.4000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Speech Communication","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167639324000815","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper provides a structured examination of Arabic Automatic Speech Recognition (ASR), focusing on the complexity posed by the language’s diverse forms and dialectal variations. We first explore the Arabic language forms, delimiting the challenges encountered with Dialectal Arabic, including issues such as code-switching and non-standardized orthography and, thus, the scarcity of large annotated datasets. Subsequently, we delve into the landscape of Arabic resources, distinguishing between Modern Standard Arabic (MSA) and Dialectal Arabic (DA) Speech Resources and highlighting the disparities in available data between these two categories. Finally, we analyze both traditional and modern approaches in Arabic ASR, assessing their effectiveness in addressing the unique challenges inherent to the language. Through this comprehensive examination, we aim to provide insights into the current state and future directions of Arabic ASR research and development.
本文对阿拉伯语自动语音识别（ASR）进行了结构化研究，重点关注该语言的多种形式和方言变化所带来的复杂性。我们首先探讨了阿拉伯语的语言形式，划分了方言阿拉伯语所遇到的挑战，包括代码转换和非标准化正字法等问题，以及大型注释数据集的稀缺性。随后，我们深入探讨了阿拉伯语资源的现状，区分了现代标准阿拉伯语 (MSA) 和方言阿拉伯语 (DA) 语音资源，并强调了这两个类别之间可用数据的差异。最后，我们分析了阿拉伯语 ASR 的传统和现代方法，评估了它们在应对阿拉伯语固有的独特挑战方面的有效性。通过这种全面的研究，我们旨在为阿拉伯语 ASR 研究和发展的现状和未来方向提供见解。
期刊介绍：
Speech Communication is an interdisciplinary journal whose primary objective is to fulfil the need for the rapid dissemination and thorough discussion of basic and applied research results.
The journal''s primary objectives are:
• to present a forum for the advancement of human and human-machine speech communication science;
• to stimulate cross-fertilization between different fields of this domain;
• to contribute towards the rapid and wide diffusion of scientifically sound contributions in this domain.
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