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

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

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

  • ISBN

    978-1-72816-376-5

  • ISSN

  • e-ISSN

  • Number of pages

    4

  • Pages from-to

    10-13

  • Publisher name

    IEEE

  • Place of publication

    New York

  • Event location

    Milan, Italy

  • Event date

    Jul 7, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000577106400003