Full metadata record
| DC pole | Hodnota | Jazyk |
|---|---|---|
| dc.contributor.author | Hanzlíček, Zdeněk | |
| dc.contributor.author | Vít, Jakub | |
| dc.date.accessioned | 2021-03-29T10:00:17Z | - |
| dc.date.available | 2021-03-29T10:00:17Z | - |
| dc.date.issued | 2020 | |
| dc.identifier.citation | HANZLÍČEK, Z. VÍT, J. LSTM-Based Speech Segmentation Trained on Different Foreign Languages. In: Text, Speech, and Dialogue 23rd International Conference, TSD 2020, Brno, Czech Republic, September 8-11, 2020, Proceedings. Cham: Springer Nature Switzerland AG, 2020. s. 456-464. ISBN 978-3-030-58322-4, ISSN 0302-9743. | cs |
| dc.identifier.isbn | 978-3-030-58322-4 | |
| dc.identifier.issn | 0302-9743 | |
| dc.identifier.uri | 2-s2.0-85091145791 | |
| dc.identifier.uri | http://hdl.handle.net/11025/43117 | |
| dc.format | 9 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | en |
| dc.publisher | Springer Nature Switzerland AG | en |
| dc.relation.ispartofseries | Text, Speech, and Dialogue 23rd International Conference, TSD 2020, Brno, Czech Republic, September 8-11, 2020, Proceedings | en |
| dc.rights | Plný text není přístupný. | cs |
| dc.rights | © Springer | en |
| dc.title | LSTM-Based Speech Segmentation Trained on Different Foreign Languages | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | conferenceObject | en |
| dc.rights.access | closedAccess | en |
| dc.type.version | publishedVersion | en |
| dc.description.abstract-translated | This paper describes experiments on speech segmentation by using bidirectional LSTM neural networks. The networks were trained on various languages (English, German, Russian and Czech), segmentation experiments were performed on 4 Czech professional voices. To be able to use various combinations of foreign languages, we defined a reduced phonetic alphabet based on IPA notation. It consists of 26 phones, all included in all languages. To increase the segmentation accuracy, we applied an iterative procedure based on detection of improperly segmented data and retraining of the network. Experiments confirmed the convergence of the procedure. A comparison with a reference HMM-based segmentation with additional manual corrections was performed. | en |
| dc.subject.translated | Speech segmentation | en |
| dc.subject.translated | Neural networks | en |
| dc.subject.translated | LSTM | en |
| dc.identifier.doi | 10.1007/978-3-030-58323-1_49 | |
| dc.type.status | Peer-reviewed | en |
| dc.identifier.obd | 43930257 | |
| dc.project.ID | GA19-19324S/Plně trénovatelná syntéza české řeči z textu s využitím hlubokých neuronových sítí | cs |
| dc.project.ID | SGS-2019-027/Inteligentní metody strojového vnímání a porozumění 4 | cs |
| dc.project.ID | LM2018140/E-infrastruktura CZ | cs |
| Vyskytuje se v kolekcích: | Konferenční příspěvky / Conference papers (NTIS) Konferenční příspěvky / Conference Papers (KKY) OBD | |
Soubory připojené k záznamu:
| Soubor | Velikost | Formát | |
|---|---|---|---|
| Hanzlíček-Vít2020_Chapter_LSTM-BasedSpeechSegmentationTr.pdf | 551,95 kB | Adobe PDF | Zobrazit/otevřít Vyžádat kopii |
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