Arabic speech recognition using deep learning and common voice dataset

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dc.contributor.authorOukas, Nourredine
dc.contributor.authorZerrouki, Taha
dc.contributor.authorHaboussi, Samia
dc.contributor.authorDjettou, Halima
dc.date.accessioned2023-03-30T08:28:48Z
dc.date.available2023-03-30T08:28:48Z
dc.date.issued2022
dc.description.abstractSpeech 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 modelen_US
dc.identifier.isbn978-166545193-2
dc.identifier.urihttps://ieeexplore.ieee.org/document/9990834
dc.identifier.uriDOI: 10.1109/3ICT56508.2022.9990834
dc.identifier.urihttps://dspace.univ-boumerdes.dz/handle/123456789/11263
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2022;pp. 642-647
dc.subjectArabic languageen_US
dc.subjectAutomatic Speech recognitionen_US
dc.subjectDeep learningen_US
dc.subjectMozilla Common Voiceen_US
dc.titleArabic speech recognition using deep learning and common voice dataseten_US
dc.typeOtheren_US

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