Sequence-to-Sequence CNN-BiLSTM Based 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%2F22%3A43966100" target="_blank" >RIV/49777513:23520/22:43966100 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-20650-4_9" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-20650-4_9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-20650-4_9" target="_blank" >10.1007/978-3-031-20650-4_9</a>
Alternative languages
Result language
angličtina
Original language name
Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech
Original language description
In this paper, we propose to frame glottal closure instant (GCI) de- tection from raw speech as a sequence-to-sequence prediction problem and to explore the potential of recurrent neural networks (RNNs) to handle this prob- lem. We compare the RNN architecture to widely used convolutional neural net- works (CNNs) and to some other machine learning-based and traditional non- learning algorithms on several publicly available databases. We show that the RNN architecture improves GCI detection. The best results were achieved for a joint CNN-BiLSTM model in which RNN is composed of bidirectional long short-term memory (BiLSTM) units and CNN layers are used to extract relevant features.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Neural Networks in Pattern Recognition; 10th IAPR TC3 Workshop, ANNPR 2022; Dubai, United Arab Emirates, November 24-26, 2022; Proceedings
ISBN
978-3-031-20649-8
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
14
Pages from-to
107-120
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
Dubai, United Arab Emirates
Event date
Nov 24, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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