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Statistical Pronunciation Adaptation for Spontaneous Speech Synthesis

  • Raheel Qader
  • Gwénolé LecorvéEmail author
  • Damien Lolive
  • Marie Tahon
  • Pascale Sébillot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10415)

Abstract

To bring more expressiveness into text-to-speech systems, this paper presents a new pronunciation variant generation method which works by adapting standard, i.e., dictionary-based, pronunciations to a spontaneous style. Its strength and originality lie in exploiting a wide range of linguistic, articulatory and prosodic features, and in using a probabilistic machine learning framework, namely conditional random fields and phoneme-based n-gram models. Extensive experiments on the Buckeye corpus of English conversational speech demonstrate the effectiveness of the approach through objective and perceptual evaluations.

Keywords

Speech synthesis Spontaneous speech Pronunciation modeling Statistical adaptation Conditional random field 

Notes

Acknowledgments

This study has been realized under the ANR (French National Research Agency) project SynPaFlex ANR-15-CE23-0015.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Raheel Qader
    • 1
  • Gwénolé Lecorvé
    • 1
    Email author
  • Damien Lolive
    • 1
  • Marie Tahon
    • 1
  • Pascale Sébillot
    • 2
  1. 1.IRISA/University of Rennes 1 (ENSSAT)LannionFrance
  2. 2.IRISA/INSA RennesRennesFrance

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