This paper addresses the challenge of learning to recite the Quran for
non-Arabic speakers. We explore the possibility of crowdsourcing a carefully
annotated Quranic dataset, on top of which AI models can be built to simplify
the learning process. In particular, we use the volunteer-based crowdsourcing
genre and implement a crowdsourcing API to gather audio assets. We integrated
the API into an existing mobile application called NamazApp to collect audio
recitations. We developed a crowdsourcing platform called Quran Voice for
annotating the gathered audio assets. As a result, we have collected around
7000 Quranic recitations from a pool of 1287 participants across more than 11
non-Arabic countries, and we have annotated 1166 recitations from the dataset
in six categories. We have achieved a crowd accuracy of 0.77, an inter-rater
agreement of 0.63 between the annotators, and 0.89 between the labels assigned
by the algorithm and the expert judgments.

通过众包的方式收集波斯语说唱语音数据集，并构建 AI 模型以简化非阿拉伯语学习者学习古兰经的过程。

古兰经音频数据集：非阿拉伯语者参与众包的标注朗诵

Quranic Audio Dataset: Crowdsourced and Labeled Recitation from  Non-Arabic Speakers

The Holy Book of Quran is believed to be the literal word of God (Allah) as
revealed to the Prophet Muhammad (PBUH) over a period of approximately 23
years. It is the book where God provides guidance on how to live a righteous
and just life, emphasizing principles like honesty, compassion, charity and
justice, as well as providing rules for personal conduct, family matters,
business ethics and much more. However, due to constraints related to the
language and the Quran organization, it is challenging for Muslims to get all
relevant ayahs (verses) pertaining to a matter or inquiry of interest. Hence,
we developed a Quran semantic search tool which finds the verses pertaining to
the user inquiry or prompt. To achieve this, we trained several models on a
large dataset of over 30 tafsirs, where typically each tafsir corresponds to
one verse in the Quran and, using cosine similarity, obtained the tafsir tensor
which is most similar to the prompt tensor of interest, which was then used to
index for the corresponding ayah in the Quran. Using the SNxLM model, we were
able to achieve a cosine similarity score as high as 0.97 which corresponds to
the abdu tafsir for a verse relating to financial matters.

研发了一种《古兰经语义搜索工具》，通过在超过 30 个《注释》数据集上训练多个模型，使用余弦相似度，在 Quran 中找到与用户查询或提示相关的经文。使用 SNxLM 模型，能够获得高达 0.97 的余弦相似度分数，与财务事务相关的经文对应的注释是 abdu。

古兰经对话：利用阿拉伯自然语言处理技术开发古兰经的语义搜索工具

Quranic Conversations: Developing a Semantic Search tool for the Quran  using Arabic NLP Techniques

Large Language Models are designed to understand complex Human Language. Yet,
Understanding of animal language has long intrigued researchers striving to
bridge the communication gap between humans and other species. This research
paper introduces a novel approach that draws inspiration from the linguistic
concepts found in the Quran, a revealed Holy Arabic scripture dating back 1400
years. By exploring the linguistic structure of the Quran, specifically the
components of ism, fil, and harf, we aim to unlock the underlying intentions
and meanings embedded within animal conversations using audio data. To unravel
the intricate complexities of animal language, we employ word embedding
techniques to analyze each distinct frequency component. This methodology
enables the identification of potential correlations and the extraction of
meaningful insights from the data. Furthermore, we leverage a bioacoustics
model to generate audio, which serves as a valuable resource for training
natural language processing (NLP) techniques. This Paper aims to find the
intention* behind animal language rather than having each word translation.

本文提出了一种基于古兰经语言结构的新方法，利用词嵌入技术分析动物的语音数据中的频率组件，以解锁动物交流中潜在的相关性和有意义的见解，并利用生物声学模型生成音频以培训自然语言处理技术，旨在找到动物语言背后的意图而不是每个单词的翻译。

通用语言建模代理

Universal Language Modelling agent

The Quran is the holy scripture of Islam, and its recitation is an important
aspect of the religion. Recognizing the recitation of the Holy Quran
automatically is a challenging task due to its unique rules that are not
applied in normal speaking speeches. A lot of research has been done in this
domain, but previous works have detected recitation errors as a classification
task or used traditional automatic speech recognition (ASR). In this paper, we
proposed a novel end-to-end deep learning model for recognizing the recitation
of the Holy Quran. The proposed model is a CNN-Bidirectional GRU encoder that
uses CTC as an objective function, and a character-based decoder which is a
beam search decoder. Moreover, all previous works were done on small private
datasets consisting of short verses and a few chapters of the Holy Quran. As a
result of using private datasets, no comparisons were done. To overcome this
issue, we used a public dataset that has recently been published (Ar-DAD) and
contains about 37 chapters that were recited by 30 reciters, with different
recitation speeds and different types of pronunciation rules. The proposed
model performance was evaluated using the most common evaluation metrics in
speech recognition, word error rate (WER), and character error rate (CER). The
results were 8.34% WER and 2.42% CER. We hope this research will be a baseline
for comparisons with future research on this public new dataset (Ar-DAD).

本文提出了一种端到端的深度学习模型，用于识别《古兰经》的朗诵。该模型使用了公共数据集进行评估，其识别性能表现为 8.34% 的单词错误率和 2.42% 的字符错误率。