Toward Accurate and Flexible Arabic Speech Recognition: A Comprehensive Framework

Continuous speech recognition (ASR/CSR) system for any language is crucial for the interactions between people and computers or machines. ASR systems play a crucial role in numerous applications, including voice-activated personal assistants and speech-to-text transcription. This paper mainly aims to outline the breakthroughs in CSR of the Arabic language and provide a comprehensive framework for Arabic CSR. Some aspects of speech recognition, like acoustic models, language models, and corpora, were addressed in this study. A review of current Arabic speech recognition(SR) research was conducted with a priority on Arabic-language speech. We outline the research approach, which combines both end-to-end(E2E) and modular-based approaches to improve the flexibility and performance of SR systems. The study proposed a comparhensive framework to build accurate and flexiable CSR system. This framework leverages the ensemble technique to integrate two CSR models, representing both the E2E and modular-based approaches. The study discusses audio capture, audio pre-processing techniques, feature extraction methods, and the utilisation of acoustic and language models. The study utilises machine learning techniques, including deep neural networks(DNN), hidden Markov models(HMM), transfer learning, and the ensembling of more than two models to train and optimise the ASR systems.