Synthetic Speech Evaluation by 2D GMM Classification in Pleasure-Arousal Scale

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F20%3A43959712" target="_blank" >RIV/49777513:23520/20:43959712 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9163559" target="_blank" >https://ieeexplore.ieee.org/document/9163559</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP49548.2020.9163559" target="_blank" >10.1109/TSP49548.2020.9163559</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Synthetic Speech Evaluation by 2D GMM Classification in Pleasure-Arousal Scale

Popis výsledku v původním jazyce

The paper is focused on a description of a system for automatic evaluation of synthetic speech quality based on two-dimensional detection in the Pleasure-Arousal (P-A) scale. The original speech material of a speaker used for synthesis is compared with the synthesized one to find similarities/differences between them. For continual P-A detection, the Gaussian mixture model (GMM) classifier is used. The GMM models of the P-A classes are created and trained using the sound/speech material from the database labelled directly in the P-A scale without any relation with the used original speech or the tested sentences. The basic experiments confirm the principal functionality of the developed system. Additional analysis shows the great importance of the proper selection of the number of mixtures, and the used type of the sound/speech database for GMM models building. The obtained objective evaluation results are highly correlated with the subjective ratings of human evaluators.

Název v anglickém jazyce

Synthetic Speech Evaluation by 2D GMM Classification in Pleasure-Arousal Scale

Popis výsledku anglicky

The paper is focused on a description of a system for automatic evaluation of synthetic speech quality based on two-dimensional detection in the Pleasure-Arousal (P-A) scale. The original speech material of a speaker used for synthesis is compared with the synthesized one to find similarities/differences between them. For continual P-A detection, the Gaussian mixture model (GMM) classifier is used. The GMM models of the P-A classes are created and trained using the sound/speech material from the database labelled directly in the P-A scale without any relation with the used original speech or the tested sentences. The basic experiments confirm the principal functionality of the developed system. Additional analysis shows the great importance of the proper selection of the number of mixtures, and the used type of the sound/speech database for GMM models building. The obtained objective evaluation results are highly correlated with the subjective ratings of human evaluators.

Druh

D - Stať ve sborníku

CEP obor

—

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

2020 43nd International Conference on Telecommunications and Signal Processing (TSP)

ISBN

978-1-72816-376-5

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

10-13

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Milan, Italy

Datum konání akce

7. 7. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000577106400003