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On the Comparison of Different Phrase Boundary Detection Approaches Trained on Czech TTS Speech Corpora

  • Markéta JůzováEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11096)

Abstract

The phrasing is a very important issue in the process of speech synthesis since it ensures higher naturalness and intelligibility of synthesized sentences. There are many different approaches to phrase boundary detection, including simple classification-based, HMM-based, CRF-based approaches, however, different types of neural networks are used for this task as well. The paper compares representative methods for phrasing of Czech sentences using large-scale TTS speech corpora as training data, taking only speaker-dependent phrasing issue into consideration.

Keywords

Phrase boundary Speech corpus Classification Conditional random fields Neural networks 

Notes

Acknowledgments

The work has been supported by the grant of the University of West Bohemia, project No. SGS-2016-039, and by Ministry of Education, Youth and Sports of the Czech Republic project No. LO1506. 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.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

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

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