HOLAJ, Richard and Petr POŘÍZKA. Automatic feedback on pronunciation and Anophone : a tool for L2 Czech annotation. Online. In Skarnitzl, Radek; Volín, Jan. Proceedings of the 20th International Congress of Phonetic Sciences, Prague 2023. Prague: Guarant International, 2023, p. 2721-2725. ISBN 978-80-908114-2-3.
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Basic information
Original name Automatic feedback on pronunciation and Anophone : a tool for L2 Czech annotation
Authors HOLAJ, Richard (203 Czech Republic, guarantor, belonging to the institution) and Petr POŘÍZKA (203 Czech Republic).
Edition Prague, Proceedings of the 20th International Congress of Phonetic Sciences, Prague 2023, p. 2721-2725, 5 pp. 2023.
Publisher Guarant International
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 60203 Linguistics
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW URL URL
RIV identification code RIV/00216224:14210/23:00132014
Organization unit Faculty of Arts
ISBN 978-80-908114-2-3
Keywords in English automatic feedback on pronunciation; speech recognition; annotation; Czech; e-learning
Tags rivok
Tags International impact, Reviewed
Changed by Changed by: Mgr. et Mgr. Stanislav Hasil, učo 415267. Changed: 18/2/2024 14:23.
Abstract
This paper introduces a research project that represents an innovative approach to e-learning applications targeting automatic feedback on the pronunciation of non-native speakers based on computer speech recognition (specifically for Czech). We have collected data from 187 speakers of different pronunciation levels from 36 languages, conducted a pilot project, and developed the first version of an attributive annotation system based on tagging isolated speech sounds. We briefly mention the results of this stage (especially the success rate of the trained model), which led us to change our strategy and move to the next phase of the development of the automatic speech recognition tool. In this article, we present the current and next project phases: the Anophone annotation tool, a new annotation system based on whole-word tagging (two- to four-syllable words). The result is a measurable improvement in both the model and the success rate of speech recognition.
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