The Suitability of Cloud-Based Speech Recognition Engines for Language Learning

Autor/inn/en	Daniels, Paul; Iwago, Koji
Titel	The Suitability of Cloud-Based Speech Recognition Engines for Language Learning
Quelle	In: JALT CALL Journal, 13 (2017) 3, S.229-239 (11 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1832-4215
Schlagwörter	Computer Assisted Instruction; Second Language Learning; Second Language Instruction; Computer Software; Management Systems; Accuracy; Speech Communication; Undergraduate Students; Foreign Countries; English (Second Language); Statistical Analysis; Auditory Perception; Teaching Methods; Japan + Suchen Sie Ihr Suchwort? Computer based training; Computerunterstützter Unterricht; Zweitsprachenerwerb; Fremdsprachenunterricht; Ausland; English as second language; English; Second Language; Englisch als Zweitsprache; Statistische Analyse; Auditive Wahrnehmung; Akustische Wahrnehmung; Akustik; Teaching method; Lehrmethode; Unterrichtsmethode
Abstract	As online automatic speech recognition (ASR) engines become more accurate and more widely implemented with call software, it becomes important to evaluate the effectiveness and the accuracy of these recognition engines using authentic speech samples. This study investigates two of the most prominent cloud-based speech recognition engines--Apple's Siri and Google Speech Recognition (GSR) to determine which engine would be more accurate at transcribing L2 learners' speech. The average recognition accuracy of Siri and GSR is reported using language samples of Japanese learners speaking English. The study also presents a series of computerized speech assessment tasks that were developed by the researchers using a cloud-based speech recognition engine in conjunction with Moodle, a widely used course management system. (As Provided).
Anmerkungen	JALT CALL SIG. 1-6-1 Nishiwaseda Shinjuku-ku, Tokyo, 169-8050, Japan. e-mail: journal!jaltcall.org; Web site: http://journal.jaltcall.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01