Enhanced Neural Speech Recognition of Quranic Recitations via a Large Audio Model
Source abstract
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: crossref · Quality: baseline
Research context
OpenAlex topics: Speech Recognition and Synthesis · Music and Audio Processing · Speech and Audio Processing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan
Department of Computer Science and Engineering, American University of Ras Al Khaimah, Ras Al Khaimah, United Arab Emirates
Department of Information Technology, Ajman University, Ajman, United Arab Emirates
Department of Computer Science, Jordan University of Science and Technology, Irbid 22110, Jordan
Funding
Jordan University of Science and Technology · Ajman University
References (43)
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