Pronunciation error detection method based on error rule clustering using a decision tree

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

Research output: Contribution to conferencePaperpeer-review

25 Citations (Scopus)

Abstract

We are developing a CALL system to train English pronunciation for Japanese native speakers. However, the precision of the error detection was not very high because the threshold for the detection was not optimum. To improve the detection accuracy, we propose a new method to optimize the thresholds of error detection. The proposed method makes several clusters of the pronunciation error rules, and the thresholds are determined for each cluster. An experiment was carried out to investigate the performance of the proposed method. As a result, about 90% of detection rate was obtained, which is a remarkable improvement from the conventional method.

Original languageEnglish
Pages173-176
Number of pages4
Publication statusPublished - 2005 Dec 1
Event9th European Conference on Speech Communication and Technology - Lisbon, Portugal
Duration: 2005 Sep 42005 Sep 8

Other

Other9th European Conference on Speech Communication and Technology
CountryPortugal
CityLisbon
Period05/9/405/9/8

ASJC Scopus subject areas

  • Engineering(all)

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