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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

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

verfasst von : Agustin Alonso, Daniel Erro, Eva Navas, Inma Hernaez

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

Verlag: Springer International Publishing

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Abstract

Speaker adaptation techniques use a small amount of data to modify Hidden Markov Model (HMM) based speech synthesis systems to mimic a target voice. These techniques can be used to provide personalized systems to people who suffer some speech impairment and allow them to communicate in a more natural way. Although the adaptation techniques don’t require a big quantity of data, the recording process can be tedious if the user has speaking problems. To improve the acceptance of these systems an important factor is to be able to obtain acceptable results with minimal amount of recordings. In this work we explore the performance of an adaptation method based on Frequency Warping which uses only vocalic segments according to the amount of available training data.

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Nächstes Kapitel A Dynamic FEC for Improved Robustness of CELP-Based Codec
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Metadaten
Titel
Study of the Effect of Reducing Training Data in Speech Synthesis Adaptation Based on Frequency Warping
verfasst von
Agustin Alonso
Daniel Erro
Eva Navas
Inma Hernaez
Copyright-Jahr
2016
Buch
Advances in Speech and Language Technologies for Iberian Languages

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

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

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-49169-1_1