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Using Auto-Encoder BiLSTM Neural Network for Czech Grapheme-to-Phoneme Conversion

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Text, Speech, and Dialogue (TSD 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11697))

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The crucial part of almost all current TTS systems is a grapheme-to-phoneme (G2P) conversion, i.e. the transcription of any input grapheme sequence into the correct sequence of phonemes in the given language. Unfortunately, the preparation of transcription rules and pronunciation dictionaries is not an easy process for new languages in TTS systems. For that reason, in the presented paper, we focus on the creation of an automatic G2P model, based on neural networks (NN). But, contrary to the majority of related works in G2P field, using only separate words as an input, we consider a whole phrase the input of our proposed NN model. That approach should, in our opinion, lead to more precise phonetic transcription output because the pronunciation of a word can depend on the surrounding words. The results of the trained G2P model are presented on the Czech language where the cross-word-boundary phenomena occur quite often, and they are compared to the rule-based approach.

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  1. 1.

    In SAMPA [25], the symbol J corresponds to the palatal nasal.

  2. 2.

    Note: This accuracy was counted on phoneme level and includes the padding symbols ’-’ and the \(\texttt {<break>}\) words, too.

  3. 3.

    This time, the accuracies were counted only for regular phonemes and words, without padding symbol “-” and without phrase-break words.


  1. Bičan, A.: Distribution and combinations of Czech consonants. Zeitschrift für Slawistik 56, 153–171 (2011)

    Article  Google Scholar 

  2. Bisani, M., Ney, H.: Joint-sequence models for grapheme-to-phoneme conversion. Speech Commun. 50(5), 434–451 (2008)

    Article  Google Scholar 

  3. Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) EMNLP, pp. 1724–1734. ACL (2014)

    Google Scholar 

  4. Hanzlíček, Z., Vít, J., Tihelka, D.: LSTM-based speech segmentation for TTS synthesis. In: Ekštein, K. (ed.) TSD 2019. LNAI, vol. 11697, pp. 361–372. Springer, Heidelberg (2019)

    Google Scholar 

  5. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  6. Jiampojamarn, S., Cherry, C., Kondrak, G.: Joint processing and discriminative training for letter-to-phoneme conversion. In: Proceedings of ACL-08: HLT, pp. 905–913. Association for Computational Linguistics, Columbus (2008)

    Google Scholar 

  7. Kučera, H.: The phonology of Czech, Slavistic printings and reprintings, vol. 30, ’s-Gravenhage, Mouton (1961)

    Google Scholar 

  8. Machač, P., Skarnitzl, R.: Principles of phonetic segmentation. Edition erudica, Epocha (2009)

    Google Scholar 

  9. Matoušek, J.: Building a New Czech text-to-speech system using triphone-based speech units. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2000. LNCS (LNAI), vol. 1902, pp. 223–228. Springer, Heidelberg (2000).

    Chapter  Google Scholar 

  10. Matoušek, J., Tihelka, D.: Annotation errors detection in TTS corpora. In: Proceedings of INTERSPEECH 2013, Lyon, France, pp. 1511–1515 (2013)

    Google Scholar 

  11. Matoušek, J., Tihelka, D., Šmídl, L.: On the impact of annotation errors on unit-selection speech synthesis. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2012. LNCS (LNAI), vol. 7499, pp. 456–463. Springer, Heidelberg (2012).

    Chapter  Google Scholar 

  12. Matoušek, J., Tihelka, D., Psutka, J.: Experiments with automatic segmentation for Czech speech synthesis. In: Matoušek, V., Mautner, P. (eds.) TSD 2003. LNCS (LNAI), vol. 2807, pp. 287–294. Springer, Heidelberg (2003).

    Chapter  Google Scholar 

  13. Matoušek, J., Tihelka, D., Romportl, J., Psutka, J.: Slovak unit-selection speech synthesis: creating a new Slovak voice within a Czech TTS system ARTIC. IAENG Int. J. Comput. Sci. 39, 147–154 (2012)

    Google Scholar 

  14. Matoušek, J., Kala, J.: On modelling glottal stop in Czech text-to-speech synthesis. In: Matoušek, V., Mautner, P., Pavelka, T. (eds.) TSD 2005. LNCS (LNAI), vol. 3658, pp. 257–264. Springer, Heidelberg (2005).

    Chapter  Google Scholar 

  15. Matoušek, J., Psutka, J.: ARTIC: a new czech text-to-speech system using statistical approach to speech segment database construction. In: Interspeech 2000 - ICSLP, Beijing, China, vol. 4, pp. 612–615 (2000)

    Google Scholar 

  16. Matoušek, J., Tihelka, D.: Slovak text-to-speech synthesis in ARTIC system. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2004. LNCS (LNAI), vol. 3206, pp. 155–162. Springer, Heidelberg (2004).

    Chapter  Google Scholar 

  17. Novak, J.R., Minamatsu, N., Hirose, K.: Phonetisaurus: exploring grapheme-to-phoneme conversion with joint n-gram models in the WFST framework. Natural Lang. Eng. 22(6), 907–938 (2016)

    Article  Google Scholar 

  18. Palková, Z.: Fonetika a fonologie češtiny [Phonetics and phonology of Czech], 1st edn. Univerzita Karlova, Nakladatelství Karolinum, Praha (1994)

    Google Scholar 

  19. Psutka, J., Müller, L., Matoušek, J., Radová, V.: Mluvíme s počítačem česky [Talking with Computer in Czech]. Academia, Praha (2006)

    Google Scholar 

  20. Rao, K., Peng, F., Sak, H., Beaufays, F.: Grapheme-to-phoneme conversion using long short-term memory recurrent neural networks. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4225–4229 (2015)

    Google Scholar 

  21. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Proceedings NIPS, Montreal, Canada, pp. 3104–3112 (2014)

    Google Scholar 

  22. Tihelka, D., Hanzlíček, Z., Jůzová, M., Vít, J., Matoušek, J., Grůber, M.: Current state of text-to-speech system ARTIC: a decade of research on the field of speech technologies. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2018. LNCS (LNAI), vol. 11107, pp. 369–378. Springer, Cham (2018).

    Chapter  Google Scholar 

  23. Wang, D., King, S.: Letter-to-sound pronunciation prediction using conditional random fields. IEEE Signal Process. Lett. 18(2), 122–125 (2011)

    Article  Google Scholar 

  24. Wang, Y., et al.: Tacotron: towards end-to-end speech synthesis (2017).

  25. Wells, J.C.: SAMPA computer readable phonetic alphabet. In: Gibbon, D., Moore, R., Winski, R. (eds.) Handbook of Standards and Resources for Spoken Language Systems. Mouton de Gruyter, Berlin (1997)

    Google Scholar 

  26. Wu, Y., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation. CoRR abs/1609.08144 (2016).

  27. Yao, K., Zweig, G.: Sequence-to-sequence neural net models for grapheme-to-phoneme conversion. CoRR abs/1506.00196 (2015)

    Google Scholar 

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This research was supported by the Czech Science Foundation (GA CR), project No. GA19-19324S, and by the grant of the University of West Bohemia, project No. SGS-2019-027.

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Correspondence to Markéta Jůzová .

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Jůzová, M., Vít, J. (2019). Using Auto-Encoder BiLSTM Neural Network for Czech Grapheme-to-Phoneme Conversion. In: Ekštein, K. (eds) Text, Speech, and Dialogue. TSD 2019. Lecture Notes in Computer Science(), vol 11697. Springer, Cham.

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