Speech and Language Disorders: A systematic review of corpora and future directions

IF 2.1
Applied Corpus Linguistics Pub Date : 2026-04-01 Epub Date: 2026-01-08 DOI:10.1016/j.acorp.2025.100186
Abeer Z. Al-Marridi , Samawiyah M. Ulde , Ahmed Bensaid , Tariq A. Khwaileh
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引用次数: 0

Abstract

Speech and Language Disorders (SLDs) significantly impact social interaction, communication, and educational outcomes, making them a global health priority. According to data published by Komodo Health, speech disorder diagnoses among children aged 0–12 increased by 110% in 2022, reaching 1.2 million cases compared to the pre-pandemic average of 570,000. Addressing this growing challenge requires empowering the research community with diverse and comprehensive corpora to drive investigations and develop innovative tools. This paper systematically reviews existing SLD corpora, evaluating their relevance to research and technological innovation. The corpora are categorized based on target population, language, data modality, and task domain. Thirteen SLDs are explored, including neurological language breakdown, motor speech disorders, child language impairments, and communication challenges in autism spectrum disorder. The review identifies key research directions in the field of SLD and highlights critical gaps and challenges using statistical insights drawn from the analyzed search. Emerging trends such as multimodal data integration and artificial intelligence applications for advanced data analysis are emphasized. The review concludes with recommendations for enhancing the utility and accessibility of SLD corpora, underscoring the importance of interdisciplinary collaboration and community engagement to address existing limitations. This review serves as a valuable resource for clinicians and researchers, guiding them in selecting the most suitable database/corpora to address their clinical and investigative needs while advancing the field of SLD research and innovation.
语音和语言障碍:语料库的系统回顾和未来发展方向
言语和语言障碍(SLDs)严重影响社会交往、沟通和教育成果,使其成为全球卫生重点。根据科莫多健康中心公布的数据,2022年,0-12岁儿童的语言障碍诊断增加了110%,达到120万例,而大流行前的平均水平为57万例。解决这一日益增长的挑战需要赋予研究社区多样化和全面的语料库,以推动研究和开发创新工具。本文系统地回顾了现有的SLD语料库,评估了它们与研究和技术创新的相关性。根据目标人群、语言、数据模式和任务领域对语料库进行分类。研究了13种特殊障碍,包括神经性语言障碍、运动语言障碍、儿童语言障碍和自闭症谱系障碍的沟通挑战。该综述确定了SLD领域的关键研究方向,并利用分析搜索得出的统计见解强调了关键差距和挑战。强调了多模式数据集成和人工智能应用于高级数据分析等新兴趋势。该审查最后提出了提高SLD语料库的实用性和可及性的建议,强调了跨学科合作和社区参与的重要性,以解决现有的局限性。这篇综述为临床医生和研究人员提供了宝贵的资源,指导他们选择最合适的数据库/语料库来满足他们的临床和研究需求,同时推进SLD领域的研究和创新。
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
Applied Corpus Linguistics
Applied Corpus Linguistics Linguistics and Language
CiteScore
1.30
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0.00%
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70 days
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