Pronunciation error detection for computer-assisted language learning system based on error rule clustering using a decision tree

Akinori Ito, Yen Ling Lim, Motoyuki Suzuki, Shozo Makino

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

A pronunciation error detection method based on pronunciation error clustering is proposed for computer-assisted language learning (CALL) systems. The method uses a decision-tree-based clustering algorithm, which automatically generates a decision tree from a large number of speech samples, in the mispronunciation rules. The acoustic analysis is conducted by using English and Japanese hidden Markov models (HMM), both of them are gender-dependent monophones with single Gaussian distribution functions. The method, by using different threshold for each cluster, provides marked improvement in pronunciation error detection.

Original languageEnglish
Pages (from-to)131-133
Number of pages3
JournalAcoustical Science and Technology
Volume28
Issue number2
DOIs
Publication statusPublished - 2007

Keywords

  • CALL system
  • Clustering
  • Decision tree
  • Pronunciation error detection

ASJC Scopus subject areas

  • Acoustics and Ultrasonics

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