References
- Allen, J., Hunnicutt, M., Klatt, D., Armstrong, R., & Pisoni, D. (1987). From text to speech: The MITalk system. NY: Cambridge University Press.
- Arik, S., Chrzanowski, M., Coates, A., Diamos, G., Gibiansky, A., Kang, Y., Li, X., Miller, J., Ng, A., Raiman, J., Sengupta, S., & Shoeybi, M. (2017). Deep voice: Real-time neural text-to-speech. Proceedings of the 34th International Conference on Machine Learning (ICML 2017) (pp. 1234-1252).
- Bechet, F. (2001). LIA PHON: Un systeme complet de phonetisation de textes. Traitement Automatique Des Langues, 42(1), 47-67.
- Black, A., Lenzo, K., & Pagel, V. (1998). Issues in building general letter to sound rules. 3rd ESCA Workshop on Speech Synthesis (pp. 77-80).
- Byrd, R., & Tzoukermann, E. (1988). Adapting an English morphological analyzer for French. Proceedings of the 26th Annual Meeting on Association for Computational Linguistics (pp. 1-6). Association for Computational Linguistics.
- de Mareuil, P., d'Alessandro, C., Bailly, G., Bechet, F., Garcia, M., Morel, M., Prudon, R., & Veronis, J. (2005). Evaluating the pron unciation of proper names by four French grapheme-to-phoneme converters. Proceedings of the Interspeech 2005 (pp. 1521-1524). Interspeech.
- Gruaz, C., Jacquemin, C., & Tzoukerman, E. (1996). Une approche a deux niveaux de la morphologie derivationnelle du francais. Actes du seminaire Lexique. Representations et Outils pour les bases lexicales. Morphologie Robuste, 107-114.
- Hahn, S., Vozila, P., & Bisani, M. (2012). Comparison of grapheme-to-phoneme methods on large pronunciation dictionaries and LVCSR tasks. In 13th Annual Conference of the International Speech Communication Association.
- Jiampojamarn, S., & Kondrak, G. (2010). Phoneme alignment: An exploration. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (pp. 780-788). Association for Computational Linguistics.
- Larreur, D., & Sorin, C. (1991). Quality evaluation of French text-to-speech synthesis within a task the importance of the mute "e". Proceedings of the ESCA Workshop on Speech Synthesis. Lannion. 25-28 September, 1990.
- Lecorve, G., & Lolive, D. (2015). Adaptive statistical utterance phonetization for French. Proceedings of the Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on (pp. 4864-4868). IEEE.
- Marchand, Y., & Damper, R. (2000). A multistrategy approach to improving pronunciation by analogy. Computational Linguistics, 26(2), 195-219.
- Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016). WaveNet: A generative model for raw audio. Retrieved from http://arxiv.org/abs/1609.03499 [Computing Research Repository] on September 19, 2016.
- Perennou, G., & De Calmes, M. (2000). MHATLex: Lexical resources for modelling the french pronunciation. Proceedings of the LREC 2000.
- Rao, K., Peng, F., Sak, H., & Beaufays, F. (2015). Grapheme-to-pho neme conversion using long short-term memory recurrent neural networks. Proceedings of the Acoustics, Speech and Signal Proce ssing (ICASSP), 2015 IEEE International Conference on (pp. 4225-4229). IEEE.
- Shen, J., Pang, R., Weiss, R., Schuster, M., Jaitly, N., Yang, Z., Chen, Z., Zhang, Y., Wang, Y., Skerry-Ryan, R., Saurous, R., Agiomyrgiannakis, Y., & Wu, Y. (2017). Natural TTS synthesis by conditioning WaveNet on mel spectrogram predictions. arXiv preprint arXiv:1712.05884. February 16, 2018.
- Sotelo, J., Mehri, S., Kumar, K., Santos, J., Kastner, K., Courville, A., & Bengio, Y. (2017). Char2Wav: End-to-End Speech Synthesis. Proceedings of the 5th International Conference on Learning Representations (ICLR 2017) Workshop. Retrieved from https://openreview.net/forum?id=B1VWyySKx on 18 February, 2017.
- Taylor, P. (2005). Hidden Markov models for grapheme to phoneme conversion. Proceedings of the 9th European Conference on Speech Communication and Technology.
- Taylor, P. (2009). Text-to-speech synthesis. NY: Cambridge University Press.
- Tokuda, K., Nankaku, U., Toda, T., Zen, H., Yamagishi, J., & Oura, K. (2013). Speech synthesis based on Hidden Markov models. Proceedings of IEEE (pp. 1234-1252).
- Van Den Bosch, A., & Canisius, S. (2006). Improved morpho-phonological sequence processing with constraint satisfaction inference. Proceedings of the 8th Meeting of the ACL Special Interest Group on Computational Phonology and Morphology (pp. 41-49). Association for Computational Linguistics.
- Wang, Y., Skerry-Ryan, R., Stanton, D., Wu, Y., Weiss, R., Jaitly, N., Yang, Z., Xiao, Y., Chen, Z., Bengio, S., Le, Q., Agiomyrgia nnakis, Y., Clark, R., & Saurous, R. (2017). Tacotron: Towards end-to-end speech synthesis. Retrieved from http://arxiv.org/abs/1703.10135 [Computing Research Repository] on April 6, 2017.
- Yoon, K., & Brew, C. (2006). A linguistically motivated approach to grapheme-to-phoneme conversion for Korean. Computer Speech & Language, 20(4), 357-381. https://doi.org/10.1016/j.csl.2005.03.002
- Yvon, F., De Mareuil, P., d'Alessandro, C., Auberge, V., Auberge, V., Bagein, M., Bailly, G., Bechet, F., Foukia, S., Goldman, J., Keller, E., O'Shaughnessy, D., Pagel, V., Sannier, F., Veronis, J., & Zellner, B. (1998). Objective evaluation of grapheme to phoneme conversion for text-to-speech synthesis in French. Computer Speech & Language, 12(4), 393-410. https://doi.org/10.1006/csla.1998.0104
- Zen, H., & Sak, H. (2015). Unidirectional long short-term memory recurrent neural network with recurrent output layer for low-latency speech synthesis. Proceedings of the Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on (pp. 4470-4474). IEEE.
- Zen, H., Senior, A., & Schuster, M. (2013). Statistical parametric speech synthesis using deep neural networks. Proceedings of the Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 7962-7966). IEEE.