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Study of the Effect of Reducing Training Data in Speech Synthesis Adaptation Based on Frequency Warping Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Objective Comparison of Four GMM-Based Methods for PMA-to-Speech Conversion

Authors : Daniel Erro, Inma Hernaez, Luis Serrano, Ibon Saratxaga, Eva Navas

Published in: Advances in Speech and Language Technologies for Iberian Languages

Publisher: Springer International Publishing

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Abstract

In silent speech interfaces a mapping is established between biosignals captured by sensors and acoustic characteristics of speech. Recent works have shown the feasibility of a silent interface based on permanent magnet-articulography (PMA). This paper studies the performance of four different mapping methods based on Gaussian mixture models (GMMs), typical from the voice conversion field, when applied to PMA-to-spectrum conversion. The results show the superiority of methods based on maximum likelihood parameter generation (MLPG), especially when the parameters of the mapping function are trained by minimizing the generation error. Informal listening tests reveal that the resulting speech is moderately intelligible for the database under study.

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previous chapter A Dynamic FEC for Improved Robustness of CELP-Based Codec
next chapter Adding Singing Capabilities to Unit Selection TTS Through HNM-Based Conversion
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Metadata
Title
Objective Comparison of Four GMM-Based Methods for PMA-to-Speech Conversion
Authors
Daniel Erro
Inma Hernaez
Luis Serrano
Ibon Saratxaga
Eva Navas
Copyright Year
2016
Book
Advances in Speech and Language Technologies for Iberian Languages

Print ISBN: 978-3-319-49168-4

Electronic ISBN: 978-3-319-49169-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-49169-1_3

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