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International Journal of Speech Technology
Erschienen in: International Journal of Speech Technology 4/2016

19.09.2016

Improved automatic English proficiency rating of unconstrained speech with multiple corpora

verfasst von: David O. Johnson, Okim Kang, Romy Ghanem

Erschienen in: International Journal of Speech Technology | Ausgabe 4/2016

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Abstract

The performance of machine learning classifiers in automatically scoring the English proficiency of unconstrained speech has been explored. Suprasegmental measures were computed by software, which identifies the basic elements of Brazil’s model in human discourse. This paper explores machine learning training with multiple corpora to improve two of those algorithms: prominent syllable detection and tone choice classification. The results show that machine learning training with the Boston University Radio News Corpus can improve automatic English proficiency scoring of unconstrained speech from a Pearson’s correlation of 0.677–0.718. This correlation is higher than any other existing computer programs for automatically scoring the proficiency of unconstrained speech and is approaching that of human raters in terms of inter-rater reliability.
Vorheriger Artikel Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA
Nächster Artikel A GMM/HMM model for reconstruction of missing speech spectral components for continuous speech recognition

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Metadaten
Titel
Improved automatic English proficiency rating of unconstrained speech with multiple corpora
verfasst von
David O. Johnson
Okim Kang
Romy Ghanem
Publikationsdatum
19.09.2016
Verlag
Springer US
Erschienen in
International Journal of Speech Technology / Ausgabe 4/2016
Print ISSN: 1381-2416
Elektronische ISSN: 1572-8110
DOI
https://doi.org/10.1007/s10772-016-9366-0

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