In recent years, advancements in Natural Language Processing (NLP) techniques have revolutionized the field of accessibility and exclusivity of testing, particularly for visually impaired students (VIS). CBT has shown in years back its relevance in terms of administering exams electronically, making the test process easier, providing quicker and more accurate results, and offering greater flexibility and accessibility for candidates. Yet, its relevance was not felt by the visually impaired students as they cannot access printed documents. Hence, in this paper, we present an NLP-driven Computer-Based Test guide for visually impaired students. It employs a speech technology pre-trained methods to provide real-time assistance and support to visually impaired students. The system utilizes NLP technologies to convert the text-based questions and the associated options in a machine-readable format. Subsequently, the speech technology pre-trained model processes the converted text enabling the VIS to comprehend and analyze the content. Furthermore, we validated that this pre-trained model is not perverse by testing for accuracy using sample audio datasets labels (A, B, C, D, E, F, G) to compare with the voice recordings obtained from 20 VIS which is been predicted by the system to attain values for precision, recall, and F1-scores. These metrics are used to assess the performance of the pre-trained model and have indicated that it is proficient enough to give its better performance to the evaluated system. The methodology adopted for this system is Object Oriented Analysis and Design Methodology (OOADM) where Objects are discussed and built by modeling real-world instances.

翻译：近年来，自然语言处理（NLP）技术的进步彻底改变了测试的可及性与包容性领域，特别是对视障学生（VIS）。计算机化测试（CBT）在过去几年中已展现出其重要性，通过电子方式管理考试，简化测试流程，提供更快更准确的结果，并为考生带来更高的灵活性与可及性。然而，视障学生因无法访问打印文档而未能感受到其相关性。因此，本文提出了一种面向视障学生的NLP驱动计算机化测试指南。该系统采用语音技术预训练方法，为视障学生提供实时辅助与支持。它利用NLP技术将基于文本的问题及相应选项转换为机器可读格式。随后，语音技术预训练模型处理转换后的文本，使视障学生能够理解并分析内容。此外，我们通过使用样本音频数据集标签（A、B、C、D、E、F、G）测试准确性，与从20名视障学生处获取的并由系统预测的语音记录进行比较，验证了该预训练模型不存在偏差，从而获得精确率、召回率和F1分数值。这些指标用于评估预训练模型的性能，表明其足以对评估系统提供更佳性能。本系统采用面向对象分析与设计方法（OOADM），通过建模真实世界实例来讨论和构建对象。