Open Access
Issue
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
Article Number 00057
Number of page(s) 12
DOI https://doi.org/10.1051/bioconf/20249700057
Published online 05 April 2024
  • T. Yuanhe, L. Renze, P. Xiangyu W. Lianxi J. Shengyi and S. Yan, ”Improving English-Arabic Transliteration with Phonemic Memories”, Findings of the Association for Computational Linguistics, pages 3262–3272, 2022. [Google Scholar]
  • K. Nahar, H. Al-Muhtaseb, W. Al-Khatib, M. Elshafei and M. Alghamdi, “Arabic Phonemes Transcription using Data Driven Approach.,” International Arab Journal of Information Technology (IAJIT), Vol. 12, 2015. [Google Scholar]
  • F. Alshuwaier and A. Areshey, “Translating English Names to Arabic Using Phonotactic Rules”, 25th Pacific Asia Conference on Language, Information and Computation, pages 485–492, 2011. [Google Scholar]
  • Ş.-A. Toma, A. Stan, M.-L. Pura and T. Bârsan, “MaRePhoR—An open access machine-readable phonetic dictionary for Romanian,” in 2017 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), 2017. [Google Scholar]
  • B. Lõrincz, “Concurrent phonetic transcription, lexical stress assignment and syllabification with deep neural networks,” Procedia Computer Science, Vol. 176, pp. 108–117, 2020. [CrossRef] [Google Scholar]
  • J. L. Lee, L. F. Ashby, M. E. Garza, Y. Lee-Sikka, S. Miller, A. Wong, A. D. McCarthy and K. Gorman, “Massively multilingual pronunciation modeling with WikiPron,” in Proceedings of the Twelfth Language Resources and Evaluation Conference, 2020. [Google Scholar]
  • K. Rao, F. Peng, H. Sak and F. Beaufays, “Grapheme-to-phoneme conversion using long short-term memory recurrent neural networks,” in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015. [Google Scholar]
  • L. Loots and T. Niesler, “Data-driven phonetic comparison and conversion between South African, British and American English pronunciations,” in Tenth Annual Conference of the International Speech Communication Association, 2009. [Google Scholar]
  • S. Yolchuyeva, G. N'emeth and B. Gyires-T'oth, “Grapheme-to-phoneme conversion with convolutional neural networks,” Applied Sciences, Vol. 9, p. 1143, 2019. [CrossRef] [Google Scholar]
  • A. P. Saucedo, A. S. Sepúlveda and D. F. Gómez Cajas, “Phoneme Recognition System Using Articulatory-Type Information,” Tecciencia, Vol. 10, pp. 11–14, 2015. [CrossRef] [Google Scholar]
  • A. Freeman, S. Condon and C. Ackerman, “Cross linguistic name matching in English and Arabic,” in Proceedings of the Human Language Technology Conference of the NAACL, Main Conference, 2006. [Google Scholar]
  • K. Yao and G. Zweig, “Sequence-to-sequence neural net models for grapheme-to-phoneme conversion,” arXiv preprint arXiv:1506.00196, 2015. [Google Scholar]
  • E. Engelhart, M. Elyasi and G. Bharaj, “Grapheme-to-phoneme transformer model for transfer learning dialects,” arXiv preprint arXiv:2104.04091, 2021 [Google Scholar]

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