TY - GEN
T1 - Analysis and Estimation of Sentence Speakability for English Pronunciation Evaluation
AU - Naijo, Satsuki
AU - Chiba, Yuya
AU - Nose, Takashi
AU - Ito, Akinori
N1 - Funding Information:
A part of this work was supported by the JSPS Grant-in-Aid for Scientific Research JP17H00823.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/13
Y1 - 2020/10/13
N2 - Conventional automatic English evaluation systems used only acoustic features extracted from an input speech. However, not only the speaker' s pronunciation ability but also the difficulty of the sentences affect the pronunciation score. In this paper, we referred to the difficulty of sentences as 'speakability.' Analysis of variance revealed that sentence speakability was effective in the evaluation as well as the speaker s pronunciation ability. We also showed that the number of phonemes in a sentence and word familiarity were useful as features to estimate sentence speakability. Finally, we carried out the multivariate regression analysis to estimate the sentence speakability score from these features, and the correlation coefficient was 0.41.
AB - Conventional automatic English evaluation systems used only acoustic features extracted from an input speech. However, not only the speaker' s pronunciation ability but also the difficulty of the sentences affect the pronunciation score. In this paper, we referred to the difficulty of sentences as 'speakability.' Analysis of variance revealed that sentence speakability was effective in the evaluation as well as the speaker s pronunciation ability. We also showed that the number of phonemes in a sentence and word familiarity were useful as features to estimate sentence speakability. Finally, we carried out the multivariate regression analysis to estimate the sentence speakability score from these features, and the correlation coefficient was 0.41.
KW - Automatic pronunciation evaluation
KW - Sentence speakability
KW - Sentence-based features
UR - http://www.scopus.com/inward/record.url?scp=85099365016&partnerID=8YFLogxK
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U2 - 10.1109/GCCE50665.2020.9292072
DO - 10.1109/GCCE50665.2020.9292072
M3 - Conference contribution
AN - SCOPUS:85099365016
T3 - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
SP - 353
EP - 355
BT - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE Global Conference on Consumer Electronics, GCCE 2020
Y2 - 13 October 2020 through 16 October 2020
ER -