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| Titre: | Arabic speech recognition using deep learning and common voice dataset |
| Auteur(s): | Oukas, Nourredine
Zerrouki, Taha
Haboussi, Samia
Djettou, Halima |
| Mots-clés: | Arabic language
Automatic Speech recognition
Deep learning
Mozilla Common Voice |
| Date de publication: | 2022 |
| Editeur: | IEEE |
| Collection/Numéro: | 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2022;pp. 642-647 |
| Résumé: | Speech 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 model |
| URI/URL: | https://ieeexplore.ieee.org/document/9990834
DOI: 10.1109/3ICT56508.2022.9990834
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/11263 |
| ISBN: | 978-166545193-2 |
| Collection(s) : | Communications Internationales
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