Stemmer and phonotactic rules to improve n-gram tagger-based indonesian phonemicization

Achanta, 2016, Analysis of sequence to sequence neural networks on grapheme to phoneme conversion task, International Joint Conference on Neural Networks (IJCNN), 2016, 2798, 10.1109/IJCNN.2016.7727552 Adriani, 2007, Stemming Indonesia: a confix-stripping approach, ACM Trans. Asian Language Inform. Process., 6, 1, 10.1145/1316457.1316459 Al-Daradkah, 2015, Automatic grapheme-to-phoneme conversion of Arabic text, Science and Information Conference (SAI), 2015, 468 Chen, H., 2020. English phonetic synthesis based on dfga g2p conversion algorithm, Vol. 1533, Institute of Physics Publishing.https://doi.org/10.1088/1742-6596/1533/3/032031. Emiru, E.D., Li, Y., Xiong, S., Fesseha, A., 2019. Speech recognition system based on deep neural network acoustic modeling for low resourced language-Amharic. In: ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 141–145. Hadj Ali, 2020, Dnn-based grapheme-to-phoneme conversion for arabic text-to-speech synthesis, Int. J. Speech Technol., 23, 569, 10.1007/s10772-020-09750-7 Hlaing, A., Pa, W., 2019. Sequence-to-sequence models for grapheme to phoneme conversion on large myanmar pronunciation dictionary, Institute of Electrical and Electronics Engineers Inc..https://doi.org/10.1109/O-COCOSDA46868.2019.9041225. Ismail, R.N., Suyanto, S., 2020. Indonesian Graphemic Syllabification Using n -Gram Tagger with State-Elimination. In: 2020 8th International Conference on Information and Communication Technology (ICoICT).https://doi.org/10.1109/ICoICT49345.2020.9166368. Jyothi, 2017, Low-resource grapheme-to-phoneme conversion using recurrent neural networks, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Liu, 2020, Agreement on target-bidirectional recurrent neural networks for sequence-to-sequence learning, J. Artif. Intell. Res., 67, 581, 10.1613/jair.1.12008 Patil, 2019, Grapheme to phoneme conversion rules for hindi, J. Adv. Res. Dyn. Control Syst., 11, 1757 Peters, B., 2017. Massively Multilingual Neural Grapheme-to-Phoneme Conversion, in: the First Workshop on Building Linguistically Generalizable NLP Systems, pp. 19–26.https://doi.org/10.18653/v1/W17-5403. Rugchatjaroen, 2019, Efficient two-stage processing for joint sequence model-based thai grapheme-to-phoneme conversion, Speech Commun., 106, 105, 10.1016/j.specom.2018.12.003 Sar, 2019, Applying linguistic g2p knowledge on a statistical grapheme-to-phoneme conversion in khmer, Elsevier B.V., 161, 415 Shareghi, 2016, Richer Interpolative Smoothing Based on Modified Kneser-Ney Language Modeling, 944 Stan, A., 2019. Input encoding for sequence-to-sequence learning of romanian grapheme-to-phoneme conversion, Institute of Electrical and Electronics Engineers Inc.https://doi.org/10.1109/SPED.2019.8906639. Suyanto, S., 2019. Incorporating syllabification points into a model of grapheme-to-phoneme conversion. Int. J. Speech Technol. 22 (2), 459–470. https://doi.org/10.1007/s10772-019-09619-4. Suyanto, Hartati, S., Harjoko, A., 2016. Modified grapheme encoding and phonemic rule to improve PNNR-based indonesian G2P. Int. J. Adv. Comput. Sci. Appl. 7 (3).https://doi.org/10.14569/IJACSA.2016.070358. Suyanto, S., Hartati, S., Harjoko, A., Compernolle, D.V., 2016. Indonesian syllabification using a pseudo nearest neighbour rule and phonotactic knowledge. Speech Commun. 85, 109–118. https://doi.org/10.1016/j.specom.2016.10.009. Švec, 2018, On the use of grapheme models for searching in large spoken archives, 6259 Yolchuyeva, S., Nmeth, G., Gyires-Tth, B., 2019. Grapheme-to-phoneme conversion with convolutional neural networks, Applied Sciences (Switzerland) 9 (6), cited By 1.https://doi.org/10.3390/app9061143. Yolchuyeva, S., Nmeth, G., Gyires-Tth, B., 2019. Transformer based grapheme-to-phoneme conversion, vol. 2019-September, Int. Speech Commun. Assoc., pp. 2095–2099.https://doi.org/10.21437/Interspeech.2019-1954.