PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

 

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Pertanika Journal of Tropical Agricultural Science, Volume J, Issue J, January J

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  • Al-Shareef, S., & Hain, T. (2012). Crf-based diacritisation of colloquial Arabic for automatic speech recognition. In Thirteenth Annual Conference of the International Speech Communication Association (pp. 1824-1827). ISCA Publishing.

  • Amdal, I., Korkmazskiy, F., & Surendran, A. C. (2000, October 16-20). Joint pronunciation modelling of non-native speakers using data-driven methods. In INTERSPEECH (pp. 622-625). Beijing, China.

  • Besacier, L., Barnard, E., Karpov, A., & Schultz, T. (2014). Automatic speech recognition for under-resourced languages: A survey. Speech Communication, 56(1), 85-100. https://doi.org/10.1016/j.specom.2013.07.008

  • Bisani, M., & Ney, H. (2002, September 16-20). Investigations on joint-multigram models for grapheme-to-phoneme conversion. In INTERSPEECH (pp. 1-4). Colorado, USA

  • Bisani, M., & Ney, H. (2008). Joint-sequence models for grapheme-to-phoneme conversion. Speech Communication, 50(5), 434-451. https://doi.org/10.1016/j.specom.2008.01.002

  • Brenzinger, M., Yamamoto, A., Aikawa, N., Koundiouba, D., Minasyan, A., Dwyer, A., Grinevald, C., Krauss, M., Miyaoka, O., Sakiyama, O., Smeets, R., & Zepeda, O. (2003, March 10-12). Language vitality and endangerment. In International Expert Meeting on the UNESCO Programme Safeguarding of Endangered Languages. Fontenoy, Paris.

  • Chen, S., Beeferman, D., & Rosenfeld, R. (1998, February 8-11). Evaluation metrics for language models. In Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop (pp. 275-280). Lansdowne, Virginia. http://repository.cmu.edu/cgi/viewcontent.cgi?article=2330&context=compsci

  • Cherifi, E. H., & Guerti, M. (2021). Arabic grapheme-to-phoneme conversion based on joint multi-gram model. International Journal of Speech Technology, 24(1), 173-182. https://doi.org/10.1007/s10772-020-09779-8

  • Chowdhury, S. A., Alam, F., Khan, N., & Noori, S. R. H. (2018). Bangla grapheme to phoneme conversion using conditional random fields. In 2017 20th International Conference of Computer and Information Technology (ICCIT) (pp. 1-6). IEEE Publishing. https://doi.org/10.1109/ICCITECHN.2017.8281780

  • Deligne, S., Yvon, F., & Bimbot, F. (1995, September 18-21). Variable-length sequence matching for phonetic transcription using joint multigrams. In Fourth European Conference on Speech Communication and Technology (pp. 2243-2246). Madrid, Spain.

  • Guazzi, M. D., Cipolla, C., Sganzerla, P., Agostoni, P. G., Fabbiocchi, F., & Pepi, M. (1983). Language vitality and endangerment. European Heart Journal, 4(Suppl. A), 181-187. https://doi.org/10.1093/eurheartj/4.suppl_a.181

  • Illina, I., Fohr, D., & Jouvet, D. (2011, August 28-31). Grapheme-to-phoneme conversion using Conditional Random Fields. In Twelfth Annual Conference of the International Speech Communication Association (pp. 2313-2316). Florence, Italy.

  • Joshi, P., Santy, S., Budhiraja, A., Bali, K., & Choudhury, M. (2020). The state and fate of linguistic diversity and inclusion in the NLP world. arXiv Preprint. https://doi.org/10.18653/v1/2020.acl-main.560

  • Juan, S., & Flora, S. (2015). Exploiting resources from closely-related languages for automatic speech recognition in low-resource languages from Malaysia (Doctoral dissertation). Université Grenoble Alpes, France. https://www.theses.fr/2015GREAM061

  • Juan, S. S., & Besacier, L. (2013, October 14-18). Fast bootstrapping of grapheme to phoneme system for under-resourced languages-application to the iban language. In Proceedings of the 4th Workshop on South and Southeast Asian Natural Language Processing (pp. 1-8). Nagoya, Japan.

  • Juan, S. S., Besacier, L., Lecouteux, B., & Dyab, M. (2015, September 6-10). Using resources from a closely-related language to develop ASR for a very under-resourced language: A case study for iban. In Proceedings of the Annual Conference of the International Speech Communication Association (pp. 1270-1274). Dresden, Germany.

  • Jurafsky, D., & Martin, J. (2000). Speech & Language Processing. Pearson Education India.

  • Karanasou, P. (2013). Phonemic variability and confusability in pronunciation modeling for automatic speech recognition (Doctoral dissertation). Université Paris Sud-Paris, France. http://hal.archives-ouvertes.fr/tel-00843589/

  • Lafferty, J., McCallum, A., & C.N. Pereira, F. (2001). Conditional rndom fileds: Probabbilistic models for segmenting and labeling sequence data. In Proceedings of the 18th International Conference on Machine Learning 2001 (ICML 2001) (pp. 282-289). ACM Publishing. https://doi.org/10.29122/mipi.v11i1.2792

  • Laurent, A., Meignier, S., & Deléglise, P. (2014). Improving recognition of proper nouns in ASR through generating and filtering phonetic transcriptions. Computer Speech & Language, 28(4), 979-996. https://doi.org/10.1016/j.csl.2014.02.006

  • Lukeš, D., Kopřivová, M., Komrsková, Z., & Poukarová, P. (2018, May 7-12). Pronunciation variants and ASR of colloquial speech: A case study on Czech. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) (pp. 2704-2709). Miyazaki, Japan.

  • Masmoudi, A., Ellouze, M., Bougares, F., Esètve, Y., & Belguith, L. (2016). Conditional random fields for the tunisian dialect grapheme-to-phoneme conversion. In Proceedings of the Annual Conference of the International Speech Communication Association (pp. 1457-1461). ISCA Publishing. https://doi.org/10.21437/Interspeech.2016-1320

  • McCallum, A. (2012). Efficiently Inducing Features of Conditional Random Fields. arXiv Preprint. http://arxiv.org/abs/1212.2504

  • Morris, J. J. (2010). A study on the use of conditional random fields for automatic speech recognition (Doctoral dissertation). The Ohio State University, USA. https://etd.ohiolink.edu/apexprod/rws_olink/r/1501/10?clear=10&p10_accession_num=osu1274212139

  • Omar, A. (1981). The Iban language of Sarawak; A grammatical description. Kuala Lumpur: Dewan Bahasa dan Pustaka.

  • Ramli, I., Jamil, N., Seman, N., & Ardi, N. (2015). An improved syllabification for a better Malay language text-to-speech synthesis (TTS). Procedia Computer Science, 76, 417-424. https://doi.org/10.1016/j.procs.2015.12.280

  • Rugchatjaroen, A., Saychum, S., Kongyoung, S., Chootrakool, P., Kasuriya, S., & Wutiwiwatchai, C. (2019). Efficient two-stage processing for joint sequence model-based Thai grapheme-to-phoneme conversion. Speech Communication, 106, 105-111. https://doi.org/10.1016/j.specom.2018.12.003

  • Saychum, S., Kongyoung, S., Rugchatjaroen, A., Chootrakool, P., Kasuriya, S., & Wutiwiwatchai, C. (2016, September 8-12). Efficient Thai grapheme-to-phoneme conversion using CRF-based joint sequence modeling. In Proceedings of the Annual Conference of the International Speech Communication Association (pp. 1462-1466). ISCA Publishing. https://doi.org/10.21437/Interspeech.2016-621

  • Shin, C. (2021). Iban as a koine language in Sarawak. Wacana, 22(1), 102-124. https://doi.org/10.17510/wacana.v22i1.985

  • Singh, A. K. (2008). Natural language processing for less privileged languages: Where do we come from? Where are we going? In Proceedings of the IJCNLP-08 Workshop on NLP for Less Privileged Languages (pp. 7-12). Asian Federation of Natural Language Processing. http://www.aclweb.org/anthology/I08-3004

  • Stadtschnitzer, M., & Schmidt, C. (2018, May 7-12). Data-driven pronunciation modeling of swiss german dialectal speech for automatic speech recognition. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) (pp. 3152-3156). Miyazaki, Japan.

  • Sutlive, V. H. (1994). A handy Reference Dictionary of Iban and English. Tun Jugah Foundation.

  • Tjalve, M., & Huckvale, M. (2005, September 4-8). Pronunciation variation modelling using accent features. In 9th European Conference on Speech Communication and Technology (pp. 1341-1344). Lisbon, Portugal.

  • Tsuboi, Y., Kashima, H., Mori, S., Oda, H., & Matsumoto, Y. (2008, August 18-22). Training conditional random fields using incomplete annotations. In Coling 2008 - 22nd International Conference on Computational Linguistics, Proceedings of the Conference (pp. 897-904). Manchester, UK. https://doi.org/10.3115/1599081.1599194

  • Tan, T. P., Xiao, X., Tang, E. K., Chng, E. S., & Li, H. (2009). MASS: A Malay language LVCSR corpus resource. In 2009 Oriental COCOSDA International Conference on Speech Database and Assessments (pp. 25-30). IEEE Publishing. https://doi.org/10.1109/ICSDA.2009.5278382.

  • Wang, X., & Sim, K. C. (2013). Integrating conditional random fields and joint multi-gram model with syllabic features for grapheme-to-phone conversion. In INTERSPEECH (pp. 2321-2325). ISCA Publishing.

  • Yamazaki, M., Morita, H., Komiya, K., & Kotani, Y. (2014). Extracting the translation of anime titles from web corpora using CRF. In Knowledge-Based Software Engineering: 11th Joint Conference, JCKBSE 2014 (pp. 311-320). Springer International Publishing. https://doi.org/10.1007/978-3-319-11854-3_26

  • Yolchuyeva, S., Németh, G., & Gyires-Tóth, B. (2019). Grapheme-to-phoneme conversion with convolutional neural networks. Applied Sciences, 9(6), 1-17. https://doi.org/10.3390/app9061143

  • Young, S. R. (1994, April). Detecting misrecognitions and out-of-vocabulary words. In Proceedings of ICASSP’94. IEEE International Conference on Acoustics, Speech and Signal Processing (Vol. 2, pp. II-21). IEEE Publishing. https://doi.org/10.1109/ICASSP.1994.389728

  • Yu, M., Nguyen, H. D., Sokolov, A., Lepird, J., Sathyendra, K. M., Choudhary, S., Mouchtaris, A., & Kunzmann, S. (2020). Multilingual grapheme-to-phoneme conversion with byte representation. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 8234-8238). IEEE Publishing. https://doi.org/10.1109/ICASSP40776.2020.9054696

  • Zoubir, A. M., & Iskander, D. R. (2007). Bootstrap methods and applications: A tutorial for the signal processing practitioner. IEEE Signal Processing Magazine, 24(4), 10-19. https://doi.org/10.1109/MSP.2007.4286560

  • Zweig, G., & Nguyen, P. (2009). Maximum mutual information multi-phone units in direct modeling. In Tenth Annual Conference of the International Speech Communication Association (pp. 1919-1922). ISCA Publishing.

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