Artefact Determination by GMM-Based Continuous Detection of Emotional Changes in Synthetic Speech

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43956317" target="_blank" >RIV/49777513:23520/19:43956317 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8768826" target="_blank" >https://ieeexplore.ieee.org/document/8768826</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2019.8768826" target="_blank" >10.1109/TSP.2019.8768826</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Artefact Determination by GMM-Based Continuous Detection of Emotional Changes in Synthetic Speech

Popis výsledku v původním jazyce

The paper is focused on a description of a system for automatic detection of speech artefacts based on the Gaussian mixture model (GMM) classifier. The system enables to detect one or more artefacts in synthetic speech produced by a text-to-speech system. Our speech artefact detection uses continual GMM classification of emotional states in 2-D affective space of valence and arousal within the whole sentence and calculates the final change in the evaluated emotions. The detected shift to negative emotions indicates presence of an artefact in the analysed sentence. The basic experiments confirm functionality of the developed system producing results with sufficient correctness of artefact detection. These results are comparable to those attained by a standard listening test method. Additional investigations show relatively great influence of the number of mixtures, the number of used emotional classes, and types of speech features on the evaluated emotional shift.

Název v anglickém jazyce

Artefact Determination by GMM-Based Continuous Detection of Emotional Changes in Synthetic Speech

Popis výsledku anglicky

The paper is focused on a description of a system for automatic detection of speech artefacts based on the Gaussian mixture model (GMM) classifier. The system enables to detect one or more artefacts in synthetic speech produced by a text-to-speech system. Our speech artefact detection uses continual GMM classification of emotional states in 2-D affective space of valence and arousal within the whole sentence and calculates the final change in the evaluated emotions. The detected shift to negative emotions indicates presence of an artefact in the analysed sentence. The basic experiments confirm functionality of the developed system producing results with sufficient correctness of artefact detection. These results are comparable to those attained by a standard listening test method. Additional investigations show relatively great influence of the number of mixtures, the number of used emotional classes, and types of speech features on the evaluated emotional shift.

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

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

2019 42nd International Conference on Telecommunications and Signal Processing (TSP)

ISBN

978-1-72811-864-2

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

45-48

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Budapest, Hungary

Datum konání akce

1. 7. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000493442800010