Synthetic Speech Evaluation by Differential Maps in Pleasure-Arousal Space

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Synthetic Speech Evaluation by Differential Maps in Pleasure-Arousal Space

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Synthetic Speech Evaluation by Differential Maps in Pleasure-Arousal Space

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GA19-19324S" target="_blank" >GA19-19324S: Plně trénovatelná syntéza české řeči z textu s využitím hlubokých neuronových sítí</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

Počet stran výsledku

11

Strana od-do

424-434

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

St. Petersburg, Russia

Datum konání akce

7. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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