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Detection of Large Segmentation Errors with Score Predictive Model

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

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References

  1. 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. 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) CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 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. 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)CrossRefGoogle Scholar
  7. 7.
    Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)zbMATHGoogle Scholar
  8. 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

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Cybernetics, Faculty of Applied SciencesUniversity of West BohemiaPilsenCzech Republic
  2. 2.New Technologies for the Information Society, Faculty of Applied SciencesUniversity of West BohemiaPilsenCzech Republic

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