Analysis and Estimation of Sentence Speakability for English Pronunciation Evaluation

Satsuki Naijo, Yuya Chiba, Takashi Nose, Akinori Ito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages353-355
Number of pages3
ISBN (Electronic)9781728198026
DOIs
Publication statusPublished - 2020 Oct 13
Event9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan
Duration: 2020 Oct 132020 Oct 16

Publication series

Name2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

Conference9th IEEE Global Conference on Consumer Electronics, GCCE 2020
CountryJapan
CityKobe
Period20/10/1320/10/16

Keywords

  • Automatic pronunciation evaluation
  • Sentence speakability
  • Sentence-based features

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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