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International Conference on Speech and Computer

SPECOM 2017: Speech and Computer pp 272–281Cite as

CRF-Based Phrase Boundary Detection Trained on Large-Scale TTS Speech Corpora

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10458))

Abstract

The paper compares different approaches in the phrase boundary detection issue, based on the data gained from speech corpora recorded for the purpose of the text-to-speech (TTS) system. It is showed that conditional random fields model can outperform basic deterministic and classification-based algorithms both in speaker-dependent and speaker independent phrasing. The results on manually annotated sentences with phrase breaks are presented here as well.

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Notes

  1. 1.

    The term ‘juncture’ was adopted from [23] and refers to required phrase breaks.

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Acknowledgement

This research was supported by Ministry of Education, Youth and Sports of the Czech Republic, project No. LO1506, and by the grant of the University of West Bohemia, project No. SGS-2016-039.

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Authors and Affiliations

  1. Department of Cybernetics and New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic

    Markéta Jůzová

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  1. Markéta Jůzová
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Correspondence to Markéta Jůzová .

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Editors and Affiliations

  1. SPIIRAS, Saint Petersburg, Russia

    Alexey Karpov

  2. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

  3. University of Hertfordshire, Hatfield, United Kingdom

    Iosif Mporas

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Jůzová, M. (2017). CRF-Based Phrase Boundary Detection Trained on Large-Scale TTS Speech Corpora. In: Karpov, A., Potapova, R., Mporas, I. (eds) Speech and Computer. SPECOM 2017. Lecture Notes in Computer Science(), vol 10458. Springer, Cham. https://doi.org/10.1007/978-3-319-66429-3_26

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  • DOI: https://doi.org/10.1007/978-3-319-66429-3_26

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  • Print ISBN: 978-3-319-66428-6

  • Online ISBN: 978-3-319-66429-3

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