  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).
