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Robust Neural Network-Based Estimation of Articulatory Features for Czech

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00221424" target="_blank" >RIV/68407700:21230/14:00221424 - isvavai.cz</a>

  • Result on the web

    <a href="http://nnw.cz/obsahy14.html#5-2014" target="_blank" >http://nnw.cz/obsahy14.html#5-2014</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.14311/NNW.2014.24.027" target="_blank" >10.14311/NNW.2014.24.027</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Robust Neural Network-Based Estimation of Articulatory Features for Czech

  • Original language description

    The article describes a neural network-based articulatory feature (AF) estimation for the Czech speech. First, the relationship between AFs and a Czech phone inventory is defined, and then the estimation based on the MLP neural networks is done. The usage of several speech representations on the input of the MLP classifiers is proposed with the purpose to obtain a robust AF estimation. The realized experiments have proved that an ANN- based AF estimation works very reliably especially in a low noise environment. Moreover, in case the number of neurons in a hidden layer is increased and if the temporal context DCT-TRAP features are used on the input of the MLP network, the AF classification works accurately also for the signals collected in the environments with a high background noise.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    JA - Electronics and optoelectronics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2014

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Neural Network World

  • ISSN

    1210-0552

  • e-ISSN

  • Volume of the periodical

    24

  • Issue of the periodical within the volume

    5

  • Country of publishing house

    CZ - CZECH REPUBLIC

  • Number of pages

    16

  • Pages from-to

    463-478

  • UT code for WoS article

    000344832300003

  • EID of the result in the Scopus database