A new framework for Arabic recitation using speech recognition and the Jaro Winkler algorithm

Automated recitation plays an important role in improving self-learning. It is based on Speech/Text recognition. The research in Arabic speech recognition is very limited. The few existing applications are only based on the Holy Qur’an. This article proposed a new system (Samee’a - ) to facilitate memorizing any kind of text such that poems, speeches and the Holy Qur’an. Samee’a system is based on Google Cloud Speech Recognition API to convert the Arabic speech to text and Jaro Winkler Distance algorithm to determine the similarity between the original and converted texts. The system has been tested using 70 collected files ranging between 12 to 400 words and some chapters from the Holy Qur’an. The average similarity achieved 83.33% for the 70 files and 69% for the selected chapters of the Holy Qur’an. These results were enhanced to 91.33 % and 95.66% after applying preprocessing operations on the text files and the Holly Qur’an respectively. To validate the obtained results, two comparison studies were performed. The Jaro Winker distance was successfully compared to the cosine and the Euclidean distance. In addition, the proposed system outperformed the related work with an improv