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Text-to-Text Transfer Transformer Phrasing Model Using Enriched Text Input

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Text, Speech, and Dialogue (TSD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13502))

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Abstract

Appropriate prosodic phrasing of the input text is crucial for natural speech synthesis outputs. The presented paper focuses on using a Text-to-Text Transfer Transformer for predicting phrase boundaries in text and inspects the possibility of enriching the input text with more detailed information to improve the success rate of the phrasing model trained on plain text. This idea came from our previous research on phrasing that showed that more detailed syntactic/semantic information might lead to more accurate predicting of phrase boundaries.

This research was supported by the Czech Science Foundation (GA CR), project No. GA21-14758S, and by the grant of the University of West Bohemia, project No. SGS-2022-017.

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Notes

  1. 1.

    Note that only the level ‘4’ phrases (prosodic/intonational phrases) were considered in our experiments; smaller ones (e.g. intermediate phrases were also labeled but not used).

  2. 2.

    The numbers in the second and the third part of the table slightly differ from those in [23] since a couple of manual corrections and amendments had been made in NRS data during the last year.

References

  1. Beckman, M.E., Ayers Elam, G.: Guidelines for ToBI Labelling, Version 3. The Ohio State University Research Foundation, Ohio State University (1997)

    Google Scholar 

  2. Bejček, E., et al.: Prague dependency treebank 3.0 (2013). http://hdl.handle.net/11858/00-097C-0000-0023-1AAF-3, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University

  3. Cruttenden, A.: Intonation. Cambridge Textbooks in Linguistics, 2nd edn. Cambridge University Press, Cambridge (1997)

    Google Scholar 

  4. Daneš, F.: Intonace a věta ve spisovné češtině. ČSAV, Praha (1957)

    Google Scholar 

  5. Fernandez, R., Rendel, A., Ramabhadran, B., Hoory, R.: Prosody contour prediction with long short-term memory, bi-directional, deep recurrent neural networks. In: Li, H., Meng, H.M., Ma, B., Chng, E., Xie, L. (eds.) INTERSPEECH, pp. 2268–2272. ISCA (2014)

    Google Scholar 

  6. Grůber, M., Matoušek, J.: Listening-test-based annotation of communicative functions for expressive speech synthesis. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2010. LNCS (LNAI), vol. 6231, pp. 283–290. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15760-8_36

    Chapter  Google Scholar 

  7. Hanzlíček, Z., Vít, J., Tihelka, D.: LSTM-based speech segmentation for TTS synthesis. In: Ekštein, K. (ed.) TSD 2019. LNCS (LNAI), vol. 11697, pp. 361–372. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27947-9_31

    Chapter  Google Scholar 

  8. Jůzová, M.: Prosodic phrase boundary classification based on Czech Speech Corpora. In: Ekštein, K., Matoušek, V. (eds.) TSD 2017. LNCS (LNAI), vol. 10415, pp. 165–173. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64206-2_19

    Chapter  Google Scholar 

  9. Jůzová, M., Tihelka, D.: Speaker-dependent BiLSTM-based phrasing. In: Sojka, P., Kopeček, I., Pala, K., Horák, A. (eds.) TSD 2020. LNCS (LNAI), vol. 12284, pp. 340–347. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58323-1_37

    Chapter  Google Scholar 

  10. Klimkov, V., et al.: Phrase break prediction for long-form reading TTS: exploiting text structure information. In: Proceedings of InterSpeech 2017, pp. 1064–1068 (2017)

    Google Scholar 

  11. Kunešová, M., Řezáčková, M.: Detection of prosodic boundaries in speech using Wav2Vec 2.0. In: Sojka, P., et al. (eds.) TSD 2022. LNCS. vol. 13502, pp. 376–387. Springer, Cham (2022)

    Google Scholar 

  12. Louw, J.A., Moodley, A.: Speaker specific phrase break modeling with conditional random fields for text-to-speech. In: 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), pp. 1–6 (2016)

    Google Scholar 

  13. Matoušek, J., Romportl, J.: On building phonetically and prosodically rich speech corpus for text-to-speech synthesis. In: Proceedings of the 2nd IASTED international conference on Computational intelligence, pp. 442–447. ACTA Press, San Francisco (2006)

    Google Scholar 

  14. 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). https://doi.org/10.1007/978-3-540-39398-6_41

    Chapter  Google Scholar 

  15. Prahallad, K., Raghavendra, E.V., Black, A.W.: Learning speaker-specific phrase breaks for text-to-speech systems. In: SSW (2010)

    Google Scholar 

  16. Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer (2020). arXiv:1910.10683

  17. Read, I., Cox, S.: Stochastic and syntactic techniques for predicting phrase breaks. Comput. Speech Lang. 21(3), 519–542 (2007)

    Article  Google Scholar 

  18. Rosenberg, A., Fernandez, R., Ramabhadran, B.: Modeling phrasing and prominence using deep recurrent learning. In: InterSpeech 2015. pp. 3066–3070. ISCA (2015)

    Google Scholar 

  19. Taylor, P.: Text-to-Speech Synthesis, 1st edn. Cambridge University Press, New York (2009)

    Book  Google Scholar 

  20. Taylor, P., Black, A.: Assigning phrase breaks from part-of-speech sequences. Comput. Speech Lang. 12, 99–117 (1998)

    Google Scholar 

  21. 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). https://doi.org/10.1007/978-3-030-00794-2_40

    Chapter  Google Scholar 

  22. Vaswani, A., et al.: Attention is all you need (2017). arXiv:1706.03762

  23. Volín, J., Řezáčková, M., Matouřek, J.: Human and transformer-based prosodic phrasing in two speech genres. In: Karpov, A., Potapova, R. (eds.) SPECOM 2021. LNCS (LNAI), vol. 12997, pp. 761–772. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87802-3_68

    Chapter  Google Scholar 

  24. Volín, J.: The size of prosodic phrases in native and foreign-accented read-out monologues. Acta Universitatis Carolinae - Philologica 2, 145–158 (2019)

    Google Scholar 

  25. Wolf, T., et al.: Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38–45. Association for Computational Linguistics, Online, October 2020

    Google Scholar 

  26. Švec, J.: t5s–T5 made simple. http://github.com/honzas83/t5s (2020). Accessed 02 April 2020

  27. Švec, J., et al.: General framework for mining, processing and storing large amounts of electronic texts for language modeling purposes. Lang. Resour. Eval. 48(2), 227–248 (2013). https://doi.org/10.1007/s10579-013-9246-z

    Article  Google Scholar 

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Acknowledgements

Computational resources were supplied by the project “e-Infrastruktura CZ” (e-INFRA CZ LM2018140) supported by the Ministry of Education, Youth and Sports of the Czech Republic.

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Authors and Affiliations

  1. New Technologies for the Information Society (NTIS) and Department of Cybernetics, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic

    Markéta Řezáčková & Jindřich Matoušek

Authors
  1. Markéta Řezáčková
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  2. Jindřich Matoušek
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Corresponding author

Correspondence to Markéta Řezáčková .

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Editors and Affiliations

  1. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Petr Sojka

  2. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Aleš Horák

  3. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Ivan Kopeček

  4. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Karel Pala

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Řezáčková, M., Matoušek, J. (2022). Text-to-Text Transfer Transformer Phrasing Model Using Enriched Text Input. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2022. Lecture Notes in Computer Science(), vol 13502. Springer, Cham. https://doi.org/10.1007/978-3-031-16270-1_32

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  • DOI: https://doi.org/10.1007/978-3-031-16270-1_32

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