EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 374 (2026) EPJ Web Conf., 374 (2026) 02002 References
Open Access
Issue
EPJ Web Conf.
Volume 374, 2026
1st International Conference on Electronic, Optical Devices and Intelligent Systems (ICEODIS 2026)
Article Number 02002
Number of page(s) 7
Section Artificial Intelligence and Data Science
DOI https://doi.org/10.1051/epjconf/202637402002
Published online 24 June 2026
  1. J. Yosinski, J. Clune, Y. Bengio, H. Lipson, How transferable are features in deep neural networks? in Adv. Neural Inf. Process. Syst. (2014). [Google Scholar]
  2. K. Palanisamy, D. Singhania, A. Yao, Rethinking CNN models for audio classification. arXiv preprint arXiv:2007.11154 (2020). https://doi.org/10.48550/arXiv.2007.11154 [Google Scholar]
  3. D. Ivanko, D. Ryumin, Development of visual and audio speech recognition systems using deep neural networks, in GraphiCon, CEUR Workshop Proc. 3027 (2021). [Google Scholar]
  4. H. Boulal, M. Hamidi, J. Barkani, M. Abarkan, Data augmentation for Amazigh speech recognition using filter banks, in Lecture Notes Electr. Eng. (Springer, Singapore, 2025), pp. 3-10. [Google Scholar]
  5. K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).https://doi.org/10.48550/arXiv.1409.1556 [Google Scholar]
  6. M. Heck, M. Suzuki, T. Fukuda, G. Kurata, S. Nakamura, Ensembles of multi-scale VGG acoustic models, in Proc. Interspeech 2017, 1616-1620 (2017). https://doi.org/10.21437/Interspeech.2017-920 [Google Scholar]
  7. S. Kornblith, J. Shlens, Q.V. Le, Do better ImageNet models transfer better? in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) (2019). [Google Scholar]
  8. H. Boulal, F. Bouroumane, M. Hamidi, J. Barkani, M. Abarkan, Exploring data augmentation for Amazigh speech recognition with convolutional neural networks. Int. J. Speech Technol. 28, 53-65 (2025). https://doi.org/10.1007/s10772-024-10164-y [Google Scholar]
  9. H. Boulal, M. Hamidi, M. Abarkan, J. Barkani, Amazigh CNN speech recognition system based on mel spectrogram feature extraction method. Int. J. Speech Technol. 27, 287-296 (2024). https://doi.org/10.1007/s10772-024-10100-0 [Google Scholar]
  10. H. Boulal, M. Hamidi, J. Barkani, M. Abarkan, Enhancing Amazigh ASR through convolutional neural networks. Multimed. Tools Appl. (2024). https://doi.org/10.1007/s11042-024-20451-0 [Google Scholar]
  11. O. Zealouk, H. Satori, N. Laaidi, M. Hamidi, K. Satori, Noise effect on Amazigh digits in speech recognition system. Int. J. Speech Technol. 23, 885892 (2020). https://doi.org/10.1007/s10772-020-09764-1 [Google Scholar]
  12. M. Hamidi, H. Satori, O. Zealouk, K. Satori, Amazigh digits through interactive speech recognition system in noisy environment. Int. J. eech Technol. 23, 101-109 (2020). https://doi.org/10.1007/s10772-019-09661-2 [Google Scholar]
  13. A. Benzirar, M. Hamidi, M. Filali Bouami, Conception of speech emotion recognition methods: A review. Indones. J. Electr. Eng. Comput. Sci. 37, 1856-1864 (2025). [Google Scholar]
  14. A. Benzirar, M. Hamidi, M. Filali Bouami, Building a speech emotion recognition system using RNN, GRU and LSTM. Int. J. Speech Technol. 28, 745-759 (2025). [Google Scholar]
  15. O. Zealouk, M. Hamidi, H. Satori, K. Satori, Amazigh digits speech recognition system under noise car environment, in Proc. Embedded Syst. Artif. Intell. (ESAI) (2020). [Google Scholar]
  16. T. Sercu, V. Goel, Advances in very deep convolutional neural networks for LVCSR, in Proc. Interspeech 2016 (2016). [Google Scholar]
  17. S. Amiriparian, T. Hübner, V. Karas, M. Gerczuk, S. Ottl, B.W. Schuller, DeepSpectrumLite: A power-efficient transfer learning framework for embedded speech and audio processing from decentralized data. Front. Artif. Intell. 5, 856232 (2022). https://doi.org/10.3389/frai.2022.856232 [Google Scholar]
  18. N. Djeffal, D. Addou, H. Kheddar, S.A. Selouani, Transfer learning-based deep residual learning for speech recognition in clean and noisy environments, in Proc. Int. Conf. Telecommunications and Intelligent Syst. (ICTIS) (2024). [Google Scholar]
  19. S. Lachenani, H. Kheddar, M. Ouldzmirli, Improving pretrained YAMNet for enhanced speech command detection via transfer learning, in Proc. Int. Conf. Telecommunications and Intelligent Syst. (ICTIS) (2024). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.