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Detection of Large Segmentation Errors with Score Predictive Model

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


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.


  • 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.

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  • DOI: 10.1007/978-3-319-24033-6_59
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Correspondence to Martin Matura .

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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.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24032-9

  • Online ISBN: 978-3-319-24033-6

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