Oukas, NourredineZerrouki, TahaHaboussi, SamiaDjettou, Halima2023-03-302023-03-302022978-166545193-2https://ieeexplore.ieee.org/document/9990834DOI: 10.1109/3ICT56508.2022.9990834https://dspace.univ-boumerdes.dz/handle/123456789/11263Speech recognition is critical in creating a natural voice interface for human-to-human communication with modern digital life equipment. Smart homes, vehicles, autonomous devices in the Internet of Things, and others need to recognize various spoken languages. Meanwhile, the Arabic language has a shortage of speech recognition systems. This study comes to develop an Arabic speech-to-text tool for Arabic language. Our solution uses DeepSpeech model which is a deep learning approach and uses a data set from the Common Voice Mozilla project. The results showed a 24.3 percent Word Error Rate and a 17.6 percent character error rate. So, the proposed model reduces the Word Error Rate by 11.7% compared to Bakheet's Wav2Vec modelenArabic languageAutomatic Speech recognitionDeep learningMozilla Common VoiceOther