Academic Journal
Automatic Speech Recognition in L2 Learning: A Review Based on PRISMA Methodology.
| Τίτλος: | Automatic Speech Recognition in L2 Learning: A Review Based on PRISMA Methodology. |
|---|---|
| Συγγραφείς: | Farrús, Mireia |
| Πηγή: | Languages; Dec2023, Vol. 8 Issue 4, p242, 13p |
| Θεματικοί όροι: | Automatic speech recognition, Speech perception, Universal language, Speech, Oral communication |
| Περίληψη: | The language learning field is not exempt from benefiting from the most recent techniques that have revolutionised the field of speech technologies. L2 learning, especially when it comes to learning some of the most spoken languages in the world, is increasingly including more and more automated methods to assess linguistics aspects and provide feedback to learners, especially on pronunciation issues. On the one hand, only a few of these systems integrate automatic speech recognition as a helping tool for pronunciation assessment. On the other hand, most of the computer-assisted language pronunciation tools focus on the segmental level of the language, providing feedback on specific phonetic pronunciation, and disregarding the suprasegmental features based on intonation, among others. The current review, based on the PRISMA methodology for systematic reviews, overviews the existing tools for L2 learning, classifying them in terms of the assessment level, (grammatical, lexical, phonetic, and prosodic), and trying the explain why so few tools are nowadays dedicated to evaluate the intonational aspect. Moreover, the review also addresses the existing commercial systems, as well as the existing gap between those tools and the research developed in this area. Finally, the manuscript finishes with a discussion of the main findings and foresees future lines of research. [ABSTRACT FROM AUTHOR] |
| Copyright of Languages is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Automatic Speech Recognition in L2 Learning: A Review Based on PRISMA Methodology. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Farrús%2C+Mireia%22">Farrús, Mireia</searchLink> – Name: TitleSource Label: Source Group: Src Data: Languages; Dec2023, Vol. 8 Issue 4, p242, 13p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Automatic+speech+recognition%22">Automatic speech recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+perception%22">Speech perception</searchLink><br /><searchLink fieldCode="DE" term="%22Universal+language%22">Universal language</searchLink><br /><searchLink fieldCode="DE" term="%22Speech%22">Speech</searchLink><br /><searchLink fieldCode="DE" term="%22Oral+communication%22">Oral communication</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The language learning field is not exempt from benefiting from the most recent techniques that have revolutionised the field of speech technologies. L2 learning, especially when it comes to learning some of the most spoken languages in the world, is increasingly including more and more automated methods to assess linguistics aspects and provide feedback to learners, especially on pronunciation issues. On the one hand, only a few of these systems integrate automatic speech recognition as a helping tool for pronunciation assessment. On the other hand, most of the computer-assisted language pronunciation tools focus on the segmental level of the language, providing feedback on specific phonetic pronunciation, and disregarding the suprasegmental features based on intonation, among others. The current review, based on the PRISMA methodology for systematic reviews, overviews the existing tools for L2 learning, classifying them in terms of the assessment level, (grammatical, lexical, phonetic, and prosodic), and trying the explain why so few tools are nowadays dedicated to evaluate the intonational aspect. Moreover, the review also addresses the existing commercial systems, as well as the existing gap between those tools and the research developed in this area. Finally, the manuscript finishes with a discussion of the main findings and foresees future lines of research. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Languages is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/languages8040242 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 242 Subjects: – SubjectFull: Automatic speech recognition Type: general – SubjectFull: Speech perception Type: general – SubjectFull: Universal language Type: general – SubjectFull: Speech Type: general – SubjectFull: Oral communication Type: general Titles: – TitleFull: Automatic Speech Recognition in L2 Learning: A Review Based on PRISMA Methodology. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Farrús, Mireia IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 2226471X Numbering: – Type: volume Value: 8 – Type: issue Value: 4 Titles: – TitleFull: Languages Type: main |
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