Quran Reciter Identification: A Deep Learning Approach

Speech-based intelligent systems using deep learning are becoming increasingly important due to their wide range of applications in our routine life. Most of the efforts on voice signal processing are limited for the English language. However, little effort has focused on voice signal processing for the Arabic language or for the Quran, which is the central religious book of Islam. In this study, our objective is to develop a deep learning based speaker identification using Quran recitations. We propose the use of Bidirectional Long Short-Term Memory (BLSTM)– a type of Recurrent Neural Networks (RNNs), which are well known for being particularly suitable for speech modeling and processing–for the task of Quranic speaker identification. Our results show that our BLSTM-based Quranic speaker identification delivers significantly improved results compared to previous approaches and is also computationally less expensive.

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