Investigating the Effects of Task Type and Linguistic Background on Accuracy in Automated Speech Recognition Systems: Implications for Use in Language Assessment of Young Learners

Autor/inn/en	Hannah, L.; Kim, H.; Jang, E. E.
Titel	Investigating the Effects of Task Type and Linguistic Background on Accuracy in Automated Speech Recognition Systems: Implications for Use in Language Assessment of Young Learners
Quelle	In: Language Assessment Quarterly, 19 (2022) 3, S.289-313 (25 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Hannah, L.) ORCID (Kim, H.) ORCID (Jang, E. E.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1543-4303
DOI	10.1080/15434303.2022.2038172
Schlagwörter	Task Analysis; Artificial Intelligence; Speech Communication; Audio Equipment; Accuracy; Pronunciation; Language Variation; Scores; Culture Fair Tests; Student Characteristics; Computer Assisted Testing; Scoring; English (Second Language); Second Language Learning; Language Tests; Language Proficiency; Oral Language; Phonological Awareness; Native Language; Comparative Analysis; Elementary School Students; Story Telling; Pictorial Stimuli; Self Evaluation (Individuals); Test of English as a Foreign Language + Suchen Sie Ihr Suchwort? Aufgabenanalyse; Künstliche Intelligenz; Audio-CD; Aussprache; Sprachenvielfalt; Bewertung; English as second language; English; Second Language; Englisch als Zweitsprache; Zweitsprachenerwerb; Language test; Sprachtest; Language skill; Language skills; Sprachkompetenz; Oral interpretation; Mündlicher Sprachgebrauch; Fantasieanregung
Abstract	As a branch of artificial intelligence, automated speech recognition (ASR) technology is increasingly used to detect speech, process it to text, and derive the meaning of natural language for various learning and assessment purposes. ASR inaccuracy may pose serious threats to valid score interpretations and fair score use for all when it is exacerbated by test takers' characteristics, such as language background and accent, and assessment task type. The present study investigated the extent to which speech-to-text accuracy rates of three major ASR systems vary across different oral tasks and students' language background variables. Results indicate that task types and students' language backgrounds have statistically significant main and interaction effects on ASR accuracy. The paper discusses the implications of the study results for applying ASR to computerized assessment design and automated scoring. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01