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A Comparison of Convolutional Neural Networks for Glottal Closure Instant Detection from Raw Speech

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43962806" target="_blank" >RIV/49777513:23520/21:43962806 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/9413675" target="_blank" >https://ieeexplore.ieee.org/document/9413675</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICASSP39728.2021.9413675" target="_blank" >10.1109/ICASSP39728.2021.9413675</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    A Comparison of Convolutional Neural Networks for Glottal Closure Instant Detection from Raw Speech

  • Original language description

    In this paper, we continue to investigate the use of machine learning for the automatic detection of glottal closure instants (GCIs) from raw speech. We compare several deep one-dimensional convolutional neural network architectures on the same data and show that the InceptionV3 model yields the best results on the test set. On publicly available databases, the proposed 1D InceptionV3 outperforms XGBoost, a non-deep machine learning model, as well as other traditional GCI detection algorithms.

  • 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

    2021

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021)

  • ISBN

    978-1-72817-605-5

  • ISSN

    1520-6149

  • e-ISSN

    2379-190X

  • Number of pages

    5

  • Pages from-to

    6938-6942

  • Publisher name

    IEEE

  • Place of publication

    New York

  • Event location

    Toronto, ON, Canada

  • Event date

    Jun 6, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000704288407043