标题： 使用深度学习的《古兰经》学习者发音错误检测与纠正 显示英文标题

标题： Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning

摘要： 评估口语语言具有挑战性，而为机器学习模型量化发音指标则更加困难。 然而，对于《古兰经》来说，这一任务由于穆斯林学者制定的严格诵读规则（塔吉维德）而变得简单，从而实现了高效的评估。 尽管有这一优势，高质量标注数据的稀缺仍然是一个重大障碍。 在本工作中，我们通过引入以下内容来弥补这些差距：(1) 一个98%自动化的管道以生成高质量的《古兰经》数据集——包括：从专家诵读者收集诵读内容，使用我们微调的wav2vec2-BERT模型在停顿点（瓦夫）进行分割，对片段进行转录，通过我们新颖的Tasmeea算法对转录内容进行验证；(2) 850多小时的音频（约30万条标注话语）；(3) 一种基于自动语音识别（ASR）的发音错误检测新方法，利用我们自定义的《古兰经语音脚本》（QPS）来编码塔吉维德规则（与现代标准阿拉伯语的IPA标准不同）。 QPS使用两级脚本：（音素级）：对阿拉伯字母进行长短元音编码。（属性级）：对每个音素的发音特征进行编码。 我们进一步包含了全面的建模，我们的新型多级CTC模型在测试集上达到了0.16%的平均音素错误率（PER）。 我们已将所有代码、数据和模型作为开源发布：https://obadx.github.io/prepare-quran-dataset/ 显示英文摘要

摘要： Assessing spoken language is challenging, and quantifying pronunciation metrics for machine learning models is even harder. However, for the Holy Quran, this task is simplified by the rigorous recitation rules (tajweed) established by Muslim scholars, enabling highly effective assessment. Despite this advantage, the scarcity of high-quality annotated data remains a significant barrier. In this work, we bridge these gaps by introducing: (1) A 98% automated pipeline to produce high-quality Quranic datasets -- encompassing: Collection of recitations from expert reciters, Segmentation at pause points (waqf) using our fine-tuned wav2vec2-BERT model, Transcription of segments, Transcript verification via our novel Tasmeea algorithm; (2) 850+ hours of audio (~300K annotated utterances); (3) A novel ASR-based approach for pronunciation error detection, utilizing our custom Quran Phonetic Script (QPS) to encode Tajweed rules (unlike the IPA standard for Modern Standard Arabic). QPS uses a two-level script: (Phoneme level): Encodes Arabic letters with short/long vowels. (Sifa level): Encodes articulation characteristics of every phoneme. We further include comprehensive modeling with our novel multi-level CTC Model which achieved 0.16% average Phoneme Error Rate (PER) on the testset. We release all code, data, and models as open-source: https://obadx.github.io/prepare-quran-dataset/