Synthetic Speech Evaluation by Differential Maps in Pleasure-Arousal Space
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F20%3A43959828" target="_blank" >RIV/49777513:23520/20:43959828 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-60276-5_41" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-60276-5_41</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-60276-5_41" target="_blank" >10.1007/978-3-030-60276-5_41</a>
Alternative languages
Result language
angličtina
Original language name
Synthetic Speech Evaluation by Differential Maps in Pleasure-Arousal Space
Original language description
The paper deals with automatic evaluation of the quality of synthetic speech using Gaussian mixture models (GMM) for classification in the Pleasure-Arousal (P-A) scale and subsequently calculated 2D and 3D P-A differentials maps. The speech synthesized from the voice of a speaker is compared with the original voice of the same speaker. Three methods of speech synthesis are ordered by descending 3D perceptual distances from the original speech material. Basic experiments confirm the principal functionality of the developed system. The detailed analysis shows a great influence of the number of mixture components, the size of the processed speech material, and the type of the database for GMM creation on partial results of the continual P-A detection and the final results. The objective evaluation results are finally compared with the subjective ratings by human evaluators.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/GA19-19324S" target="_blank" >GA19-19324S: Fully Trainable Deep Neural Network Based Czech Text-to-Speech Synthesis</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Speech and Computer 22nd International Conference, SPECOM 2020, St. Petersburg, Russia, October 7-9, 2020, Proceedings
ISBN
978-3-030-60275-8
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
11
Pages from-to
424-434
Publisher name
Springer
Place of publication
Cham
Event location
St. Petersburg, Russia
Event date
Oct 7, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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