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The subspace Gaussian mixture model-A structured model for speech recognition

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F11%3APU96096" target="_blank" >RIV/00216305:26230/11:PU96096 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    The subspace Gaussian mixture model-A structured model for speech recognition

  • Original language description

    Speech recognition based on the Hidden Markov Model-Gaussian Mixture Model (HMM-GMM) framework generally involves training a completely separate GMM in each HMM state.We introduce a model in which the HMM states share a common structure but the means andmixture weights are allowed to vary in a subspace of the full parameter space, controlled by a global mapping from a vector space to the space of GMM parameters.

  • Czech name

  • Czech description

Classification

  • Type

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

  • CEP classification

    JC - Computer hardware and software

  • OECD FORD branch

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2011

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    COMPUTER SPEECH AND LANGUAGE

  • ISSN

    0885-2308

  • e-ISSN

  • Volume of the periodical

    25

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    36

  • Pages from-to

    404-439

  • UT code for WoS article

  • EID of the result in the Scopus database