Full metadata record
| DC pole | Hodnota | Jazyk |
|---|---|---|
| dc.contributor.author | Matoušek, Jindřich | |
| dc.contributor.author | Tihelka, Daniel | |
| dc.date.accessioned | 2023-02-06T11:00:19Z | - |
| dc.date.available | 2023-02-06T11:00:19Z | - |
| dc.date.issued | 2022 | |
| dc.identifier.citation | MATOUŠEK, J. TIHELKA, D. Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech. In Artificial Neural Networks in Pattern Recognition; 10th IAPR TC3 Workshop, ANNPR 2022; Dubai, United Arab Emirates, November 24-26, 2022; Proceedings. Cham: Springer Nature Switzerland AG, 2022. s. 107-120. ISBN: 978-3-031-20649-8 , ISSN: 0302-9743 | cs |
| dc.identifier.isbn | 978-3-031-20649-8 | |
| dc.identifier.issn | 0302-9743 | |
| dc.identifier.uri | 2-s2.0-85142752874 | |
| dc.identifier.uri | http://hdl.handle.net/11025/51298 | |
| dc.format | 14 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | en |
| dc.publisher | Springer Nature Switzerland AG | en |
| dc.relation.ispartofseries | Artificial Neural Networks in Pattern Recognition; 10th IAPR TC3 Workshop, ANNPR 2022; Dubai, United Arab Emirates, November 24-26, 2022; Proceedings | en |
| dc.rights | Plný text je přístupný v rámci univerzity přihlášeným uživatelům. | cs |
| dc.rights | © The Author(s), under exclusive licence to Springer Nature B.V. | en |
| dc.title | Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | ConferenceObject | en |
| dc.rights.access | restrictedAccess | en |
| dc.type.version | publishedVersion | en |
| dc.description.abstract-translated | 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. | en |
| dc.subject.translated | glottal closure instant detection | en |
| dc.subject.translated | deep learning | en |
| dc.subject.translated | recurrent neural network | en |
| dc.subject.translated | convolutional neural network | en |
| dc.identifier.doi | 10.1007/978-3-031-20650-4_9 | |
| dc.type.status | Peer-reviewed | en |
| dc.identifier.obd | 43937100 | |
| dc.project.ID | EF17_048/0007267/InteCom: VaV inteligentních komponent pokročilých technologií pro plzeňskou metropolitní oblast | cs |
| dc.project.ID | 90140/Velká výzkumná infrastruktura_(J) - e-INFRA CZ | cs |
| dc.project.ID | TL05000546/Využití multimediálního výkladového slovníku pro moderní výuku češtiny | cs |
| Vyskytuje se v kolekcích: | Konferenční příspěvky / Conference Papers (KKY) OBD | |
Soubory připojené k záznamu:
| Soubor | Velikost | Formát | |
|---|---|---|---|
| Matousek_Tihelka_Sequence-to-Sequence_ANNPR_2022.pdf | 551,79 kB | Adobe PDF | Zobrazit/otevřít Vyžádat kopii |
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