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Automatic Speech Recognition Based on Neural Networks Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Designing High-Coverage Multi-level Text Corpus for Non-professional-voice Conservation

Authors : Markéta Jůzová, Daniel Tihelka, Jindřich Matoušek

Published in: Speech and Computer

Publisher: Springer International Publishing

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Abstract

The paper focuses on building a text corpus suitable for the conservation of the voices of non-professional speakers, who are loosing their voices due to serious healthy problems. Since we do not know in advance, how many sentences a speaker will be able to record, we propose a multi-level greedy algorithm which can ensure the coverage of selected texts by various phonetic and prosodic units. The comparison of such coverage is presented for various corpus sizes, and compared to the generic TTS corpus recorded by a healthy professional speaker.

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previous chapter Design of a Speech Corpus for Research on Cross-Lingual Prosody Transfer
next chapter Designing Syllable Models for an HMM Based Speech Recognition System
Footnotes
1
We consider here a diphone as TTS system unit, i.e. signal from a middle of one phone to the middle of the next phone. Nevertheless, the numbers presented will be the same when considering a diphone as the join of any two neighbouring phones.
 
2
Let us emphasize that we work with text now, so we can only expect how the sentence will, or should be pronounced.
 
3
Let us note that the real unnatural artefacts appearance will be much lower and it will be influenced by other factors like recording style, speech units segmentation, unit selection criteria etc. Those will not be influenced by the speech corpus per se.
 
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Metadata
Title
Designing High-Coverage Multi-level Text Corpus for Non-professional-voice Conservation
Authors
Markéta Jůzová
Daniel Tihelka
Jindřich Matoušek
Copyright Year
2016
Book
Speech and Computer

Print ISBN: 978-3-319-43957-0

Electronic ISBN: 978-3-319-43958-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-43958-7_24

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