Skip to main content

Detection of Large Segmentation Errors with Score Predictive Model

  • 1631 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9302)

Abstract

This paper investigates a possibility of an utilization of regressive score predictive model (SPM) in a process of detection of large segmentation errors. SPM’s scores of automatically marked boundaries between all speech segments are examined and further elaborated in an effort to discover the best threshold to distinguish between small and large errors. It was shown that the suggested detection method with a proper threshold can be used to detect all large errors for a specific type of a boundary.

Keywords

  • Detection of segmentation errors
  • Large segmentation errors
  • Score predictive model

This research was supported by the Technology Agency of the Czech Republic, project No. TA01011264 and by the grant of the University of West Bohemia, project No. SGS-2013-032. The access to the MetaCentrum clusters provided under the programme LM2010005 is highly appreciated.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-24033-6_59
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   79.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-24033-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Matoušek, J., Romportl, J.: Automatic pitch-synchronous phonetic segmentation. In: Proceedings of 9th Annual Conference of International Speech Communication Association, INTERSPEECH 2008, Brisbane, Australia, pp. 1626–1629. ISCA (2008)

    Google Scholar 

  2. 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, vol. 7499, pp. 456–463. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  3. Lin, C.Y., Jang, J.S.: Automatic phonetic segmentation by score predictive model for the corpora of Mandarin singing voices. IEEE Transactions on Audio, Speech, and Language Processing 15(7), 2151–2159 (2007)

    CrossRef  Google Scholar 

  4. Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm

  5. Young, S.J., Evermann, G., Gales, M.J.F., Hain, T., Kershaw, D., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P.C.: HTK Book (for HTK Version 3.4). The Cambridge University, Cambridge (2006)

    Google Scholar 

  6. Legát, M., Matoušek, J., Tihelka, D.: On the detection of pitch marks using a robust multi-phase algorithm. Speech Communication 53(4), 552–566 (2011)

    CrossRef  Google Scholar 

  7. Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)

    MATH  Google Scholar 

  8. Matura, M.: Phonetic Segmentation of Speech and Possibilities of its Automatic Correction. Master’s thesis, The University of West Bohemia, Faculty of Applied Sciences, Pilsen (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Martin Matura .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Matura, M., Matoušek, J. (2015). Detection of Large Segmentation Errors with Score Predictive Model. In: Král, P., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2015. Lecture Notes in Computer Science(), vol 9302. Springer, Cham. https://doi.org/10.1007/978-3-319-24033-6_59

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24033-6_59

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24032-9

  • Online ISBN: 978-3-319-24033-6

  • eBook Packages: Computer ScienceComputer Science (R0)