Work

Enhanced Neural Speech Recognition of Quranic Recitations via a Large Audio Model

Year: 2025

Type: article

Abstract: In this work, we build on our recent work toward developing a neural speech recognition (NSR) for Quranic recitations that is accessible to people of any age, gender, or expertise level. The Quran recitations by females and males (QRFAM) dataset, a sizable benchmark dataset of audio recordings made by male and female reciters from various age groups and competence levels, was previously reported in our prior works. In addition to this dataset, we used various subsets of the QRFAM dataset for training, validation, and testing to build several basic NSR systems based on Mozilla’s DeepSpeech model. Our current efforts to optimize and enhance these baseline models have also been presented. In this study, we expand our efforts by utilizing one of the well-known speech recognition models, Whisper, and we describe the effect of this choice on the model’s accuracy, expressed as the word error rate (WER), in comparison to that of DeepSpeech.

Source: Applied Sciences

DOI: 10.3390/app15179521

Authors: Mohammad Alshboul, Abdul Rahman Al Muaitah, Suhad Al-Issa, Mahmoud Al-Ayyoub

Institutions: Jordan University of Science and Technology, American University of Ras Al Khaimah, Ajman University

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: Jordan University of Science and Technology, Ajman University