Czech parliament meeting recordings as ASR training data

• Autorzy: Krůza Jan Oldřich

Identyfikatory

DOI

10.15439/2020F119

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

I present a way to leverage the stenographed recordings of the Czech parliament meetings for purposes of training a speech-to-text system. The article presents a method for scraping the data, acquiring word-level alignment and selecting reliable parts of the imprecise transcript. Finally, I present an ASR system trained on these and other data.

Słowa kluczowe

EN

audio signal processing; feature selection; artificial intelligence; natural language processing; speech recognition; speech synthesis

PL

przetwarzanie sygnału audio; wybór funkcji; sztuczna inteligencja; przetwarzanie języka naturalnego; rozpoznawanie mowy; synteza mowy

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 185--188
• Opis fizyczny: Bibliogr. 16 poz., il., tab.

Twórcy

autor

Krůza Jan Oldřich

• Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University

Bibliografia

• 1. M. Korvas, O. Plátek, O. Dušek, L. Žilka, and F. Jurčı́ček, “Free english and czech telephone speech corpus,” 2014.
• 2. O. Plátek, O. Dušek, and F. Jurčı́ček, “Vystadial 2016 – czech data,” 2016, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University. [Online]. Available: http://hdl.handle.net/11234/1-1740
• 3. M. Mikulová, J. Mı́rovský, A. Nedoluzhko, P. Pajas, J. Štěpánek, and J. Hajič, “Pdtsc 2.0-spoken corpus with rich multi-layer structural annotation,” in International Conference on Text, Speech, and Dialogue. Springer, 2017, pp. 129–137.
• 4. J. Hajič, P. Pajas, P. Ircing, J. Romportl, N. Peterek, M. Spousta, M. Mikulová, M. Grůber, and M. Legát, “Prague DaTabase of spoken czech 1.0,” 2017, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University. [Online]. Available: http://hdl.handle.net/11234/1-2375
• 5. M. Grůber, “Czech senior COMPANION expressive speech corpus,” 2014, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University. [Online]. Available: http://hdl.handle.net/11858/00-097C-0000-0023-1D76-9
• 6. L. Šmı́dl and A. Pražák, “OVM – otázky václava moravce,” 2013, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University. [Online]. Available: http://hdl.handle.net/11858/00-097C-0000-000D-EC98-3
• 7. L. Šmı́dl, P. Stanislav, and V. Radová, “STAZKA – speech recordings from vehicles,” 2015, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University. [Online]. Available: http://hdl.handle.net/11234/1-1510
• 8. O. Krůza and N. Peterek, “Making community and asr join forces in web environment,” in International Conference on Text, Speech and Dialogue. Springer, 2012, pp. 415–421.
• 9. O. Krůza, “Spoken corpus of karel makoň,” 2012, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University. [Online]. Available: http://hdl.handle.net/11372/LRT-1455
• 10. A. Pražák, J. V. Psutka, J. Hoidekr, J. Kanis, L. Müller, and J. Psutka, “Automatic online subtitling of the czech parliament meetings,” in International Conference on Text, Speech and Dialogue. Springer, 2006, pp. 501–508.
• 11. A. Pražák and L. Šmı́dl, “Czech parliament meetings,” 2012, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University. [Online]. Available: http://hdl.handle.net/11858/00-097C-0000-0005-CF9C-4
• 12. P. J. Moreno, C. Joerg, J.-M. V. Thong, and O. Glickman, “A recursive algorithm for the forced alignment of very long audio segments,” in Fifth International Conference on Spoken Language Processing, 1998.
• 13. T. J. Hazen, “Automatic alignment and error correction of human generated transcripts for long speech recordings,” in Ninth International Conference on Spoken Language Processing, 2006.
• 14. A. Hannun, C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, R. Prenger, S. Satheesh, S. Sengupta, A. Coates et al., “Deep speech: Scaling up end-to-end speech recognition,” arXiv preprint https://arxiv.org/abs/1412.5567, 2014.
• 15. W. Byrne, J. Hajič, P. Ircing, F. Jelinek, S. Khudanpur, J. McDonough, N. Peterek, and J. Psutka, “Large vocabulary speech recognition for read and broadcast czech,” in International Workshop on Text, Speech and Dialogue. Springer, 1999, pp. 235–240.
• 16. L. Benešová, M. Křen, and M. Waclawičová, “Korpus spontánnı́ mluvené češtiny oral2013,” Časopis pro modernı́ filologii (Journal for Modern Philology), vol. 1, no. 97, pp. 42–50, 2015.

Uwagi

1. Track 1: Artificial Intelligence

2. Technical Session: 5th International Workshop on Language Technologies and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).