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
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Czech description
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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