Advertisement

On the Contribution of Articulatory Features to Speech Synthesis

  • Martin MaturaEmail author
  • Markéta Jůzová
  • Jindřich Matoušek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11096)

Abstract

There are several features that are used for the unit selection speech synthesis. Among the most used for computing a concatenation cost are energy, \(F_0\) and Mel-frequency cepstrum coefficients (MFCC) that usually give a good description of a speech signal. In our work, we focus on a usage of articulatory features. We want to determine whether they are correlated with MFCC and in that case, if they can replace MFCC or bring a new information into the process of speech synthesis. To obtain the articulatory data, we used electromagnetic articulograph AG501 and then we examined the correlation of two sequences of join costs each described by different features.

Keywords

Articulatory features Join cost Correlation Electromagnetic articulograph 

Notes

Acknowledgments

This research was supported by the Czech Science Foundation (GA CR), project No. GA16-04420S and by the grant of the University of West Bohemia, project No. SGS-2016-039. Access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated.

References

  1. 1.
    Canevari, C., Badino, L., Fadiga, L.: A new Italian dataset of parallel acoustic and articulatory data. In: INTERSPEECH. ISCA (2015)Google Scholar
  2. 2.
    Hunt, A.J., Black, A.W.: Unit selection in a concatenative speech synthesis system using a large speech database. In: ICASSP, vol. 1, pp. 373–376. IEEE (1996)Google Scholar
  3. 3.
    Jůzová, M., Tihelka, D., Matoušek, J.: Designing high-coverage multi-level text corpus for non-professional-voice conservation. In: Ronzhin, A., Potapova, R., Németh, G. (eds.) SPECOM 2016. LNCS (LNAI), vol. 9811, pp. 207–215. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-43958-7_24CrossRefGoogle Scholar
  4. 4.
    Jůzová, M., Tihelka, D., Matoušek, J., Hanzlíček, Z.: Voice conservation and TTS system for people facing total laryngectomy. In: INTERSPEECH. ISCA (2017)Google Scholar
  5. 5.
    Kaburagi, T., Wakamiya, K., Honda, M.: Three-dimensional electromagnetic articulography: a measurement principle. J. Acoust. Soc. Am. 118(1), 428–443 (2005)CrossRefGoogle Scholar
  6. 6.
    Legát, M., Matoušek, J., Tihelka, D.: A robust multi-phase pitch-mark detection algorithm. INTERSPEECH 1, 1641–1644 (2007)Google Scholar
  7. 7.
    Legát, M., Matoušek, J., Tihelka, D.: On the detection of pitch marks using a robust multi-phase algorithm. Speech Commun. 53(4), 552–566 (2011)CrossRefGoogle Scholar
  8. 8.
    Liu, Z.C., Ling, Z.H., Dai, L.R.: Articulatory-to-acoustic conversion with cascaded prediction of spectral and excitation features using neural networks. In: INTERSPEECH, pp. 1502–1506. ISCA (2016)Google Scholar
  9. 9.
    Matoušek, J., Legát, M.: Is unit selection aware of audible artifacts? In: SSW 2013, Proceedings of the 8th Speech Synthesis Workshop, pp. 267–271. ISCA, Barcelona (2013)Google Scholar
  10. 10.
    Matoušek, J., Tihelka, D.: Classification-based detection of glottal closure instants from speech signals. In: INTERSPEECH, pp. 3053–3057. ISCA (2017)Google Scholar
  11. 11.
    Matoušek, J., Tihelka, D., Romportl, J.: Current state of Czech text-to-speech system ARTIC. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2006. LNCS (LNAI), vol. 4188, pp. 439–446. Springer, Heidelberg (2006).  https://doi.org/10.1007/11846406_55CrossRefGoogle Scholar
  12. 12.
    Matoušek, J., Romportl, J.: Automatic pitch-synchronous phonetic segmentation. In: INTERSPEECH, pp. 1626–1629. ISCA (2008)Google Scholar
  13. 13.
    Richmond, K.: A multitask learning perspective on acoustic-articulatory inversion. In: INTERSPEECH, pp. 2465–2468. ISCA, August 2007Google Scholar
  14. 14.
    Richmond, K., Hoole, P., King, S.: Announcing the electromagnetic articulography (day 1) subset of the mngu0 articulatory corpus. In: INTERSPEECH. ISCA (2011)Google Scholar
  15. 15.
    Richmond, K., King, S.: Smooth talking: articulatory join costs for unit selection. In: ICASSP, pp. 5150–5154. IEEE (2016)Google Scholar
  16. 16.
    Stella, M., Stella, A., Sigona, F., Bernardini, P., Grimaldi, M., Fivela, B.G.: Electromagnetic articulography with AG500 and AG501. In: INTERSPEECH, pp. 1316–1320. ISCA (2013)Google Scholar
  17. 17.
    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: TSD. Lecture Notes in Computer Science. Springer, Heidelberg (2018)Google Scholar
  18. 18.
    Tihelka, D., Kala, J., Matoušek, J.: Enhancements of Viterbi search for fast unit selection synthesis. In: INTERSPEECH, pp. 174–177. ISCA (2010)Google Scholar
  19. 19.
    Toda, T., Black, A., Tokuda, K.: Acoustic-to-articulatory inversion mapping with gaussian mixture model. In: INTERSPEECH. ISCA (2004)Google Scholar
  20. 20.
    Toutios, A., Margaritis, K.: Acoustic-to-articulatory inversion of speech: a review. In: Proceedings of the International 12th TAINN (2003)Google Scholar
  21. 21.
    Wrench, A.: The mocha-timit articulatory database (1999). database available at http://www.cstr.ed.ac.uk/research/projects/artic/mocha.html
  22. 22.
    Wrench, A.A., Richmond, K.: Continuous speech recognition using articulatory data. In: INTERSPEECH, pp. 145–148. ISCA (2000)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Martin Matura
    • 1
    Email author
  • Markéta Jůzová
    • 1
  • Jindřich Matoušek
    • 1
  1. 1.Department of Cybernetics and New Technologies for the Information Society, Faculty of Applied SciencesUniversity of West BohemiaPilsenCzech Republic

Personalised recommendations