Efficient Personalized Mispronunciation Detection of Taiwanese-Accented English Speech Based on Unsupervised Model Adaptation and Dynamic Sentence Selection

Autor/inn/en	Wu, Chung-Hsien; Su, Hung-Yu; Liu, Chao-Hong
Titel	Efficient Personalized Mispronunciation Detection of Taiwanese-Accented English Speech Based on Unsupervised Model Adaptation and Dynamic Sentence Selection
Quelle	In: Computer Assisted Language Learning, 26 (2013) 5, S.446-467 (22 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0958-8221
DOI	10.1080/09588221.2012.687383
Schlagwörter	Pronunciation; Pronunciation Instruction; English (Second Language); Second Language Instruction; Second Language Learning; Teaching Methods; Language Teachers; Foreign Countries; Accuracy; Computer Assisted Instruction; Computational Linguistics; Models; Probability; Acoustics; Phonetics; Sentences; Speech Communication; Student Evaluation; Scoring; Linguistic Input; Taiwan + Suchen Sie Ihr Suchwort? Aussprache; Ausspracheübung; English as second language; English; Second Language; Englisch als Zweitsprache; Fremdsprachenunterricht; Zweitsprachenerwerb; Teaching method; Lehrmethode; Unterrichtsmethode; Language teacher; Sprachunterricht; Ausland; Computer based training; Computerunterstützter Unterricht; Linguistics; Computerlinguistik; Analogiemodell; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Akustik; Phonetik; Fonetik; Sentence analysis; Satzanalyse; Schulnote; Studentische Bewertung; Bewertung; Sprachbildung
Abstract	This study presents an efficient approach to personalized mispronunciation detection of Taiwanese-accented English. The main goal of this study was to detect frequently occurring mispronunciation patterns of Taiwanese-accented English instead of scoring English pronunciations directly. The proposed approach quickly identifies personalized mispronunciations of students, enabling English teachers to spend more time on teaching or rectifying student pronunciations. In this approach, an unsupervised model adaptation method was performed on the universal acoustic models to recognize the speech of a specific speaker with mispronunciations and a Taiwanese accent. A dynamic sentence selection algorithm that considers the mutual information of the related mispronunciations is proposed to select a sentence containing the most undetected mispronunciations to quickly detect personalized mispronunciations. The experimental results show that the proposed unsupervised adaptation approach obtains an accuracy improvement of approximately 2.1% in the recognition of Taiwanese-accented English speech. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10