Coarse-To-Fine And Cross-Lingual ASR Transfer
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10440570" target="_blank" >RIV/00216208:11320/21:10440570 - isvavai.cz</a>
Výsledek na webu
<a href="https://ics.upjs.sk/~antoni/ceur-ws.org/Vol-0000/paper09.pdf" target="_blank" >https://ics.upjs.sk/~antoni/ceur-ws.org/Vol-0000/paper09.pdf</a>
DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Coarse-To-Fine And Cross-Lingual ASR Transfer
Popis výsledku v původním jazyce
End-to-end neural automatic speech recognition systems achieved recently state-of-the-art results but they require large datasets and extensive computing resources. Transfer learning has been proposed to overcome these difficulties even across languages, e.g., German ASR trained from an English model. We experiment with much less related languages, reusing an English model for Czech ASR. To simplify the transfer, we propose to use an intermediate alphabet, Czech without accents, and we document that it is a highly effective strategy. The technique is also useful on Czech data alone, in the style of "coarse-to-fine" training. We achieve substantial reductions in training time as well as word error rate (WER).
Název v anglickém jazyce
Coarse-To-Fine And Cross-Lingual ASR Transfer
Popis výsledku anglicky
End-to-end neural automatic speech recognition systems achieved recently state-of-the-art results but they require large datasets and extensive computing resources. Transfer learning has been proposed to overcome these difficulties even across languages, e.g., German ASR trained from an English model. We experiment with much less related languages, reusing an English model for Czech ASR. To simplify the transfer, we propose to use an intermediate alphabet, Czech without accents, and we document that it is a highly effective strategy. The technique is also useful on Czech data alone, in the style of "coarse-to-fine" training. We achieve substantial reductions in training time as well as word error rate (WER).
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
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OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů