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USING EXTREME GRADIENT BOOSTING TO DETECT GLOTTAL CLOSURE INSTANTS IN SPEECH SIGNAL

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43956321" target="_blank" >RIV/49777513:23520/19:43956321 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/8683889" target="_blank" >https://ieeexplore.ieee.org/document/8683889</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICASSP.2019.8683889" target="_blank" >10.1109/ICASSP.2019.8683889</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    USING EXTREME GRADIENT BOOSTING TO DETECT GLOTTAL CLOSURE INSTANTS IN SPEECH SIGNAL

  • Original language description

    In this paper, we continue to investigate the use of classifiers for the automatic detection of glottal closure instants (GCIs) from the speech signal. We focus on extreme gradient boosting (XGB), a fast and powerful implementation of a gradient boosting algorithm. We show that XGB outperforms other classifiers, achieving GCI detection accuracy F 1 = 98.55% and AUC = 99.90%. The proposed XGB model is also shown to outperform other existing GCI detection algorithms on publicly available databases. Despite using much less training data, the performance of XGB is comparable to a deep convolutional neural network based approach, especially when it is tested on voices that were not included in the training data.

  • 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

    2019

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019)

  • ISBN

    978-1-4799-8131-1

  • ISSN

    1520-6149

  • e-ISSN

    2379-190X

  • Number of pages

    5

  • Pages from-to

    6515-6519

  • Publisher name

    IEEE

  • Place of publication

    New York

  • Event location

    Brighton, United Kingdom

  • Event date

    May 12, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000482554006149