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Data adaptation for Hidden Markov Model in speech recognition

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F02%3APU28123" target="_blank" >RIV/00216305:26220/02:PU28123 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Data adaptation for Hidden Markov Model in speech recognition

  • Original language description

    In Hidden Markov models, speech data are modeled by Gaussian distributions. In this paper, we propose to Gaussianize the features to better fit to this modeling. A distribution of the data is estimated and a transform function is derived. We test three methods of the transform estimation (global, speaker based, frame based) and report results on the SPINE 2000 task with Sphinx recognizer. We conclude that the proposed method is a cheap way to increase the recognition accuracy.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JA - Electronics and optoelectronics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2002

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Proceedings of 8th Conference STUDENT EEICT 2002

  • ISBN

    80-214-2116-9

  • ISSN

  • e-ISSN

  • Number of pages

    4

  • Pages from-to

    317-320

  • Publisher name

    Neuveden

  • Place of publication

    Brno

  • Event location

    FEKT VUT Brno

  • Event date

    Apr 25, 2002

  • Type of event by nationality

    CST - Celostátní akce

  • UT code for WoS article