Phoneme Set Design Using English Speech Database by Japanese for Dialogue-Based English CALL Systems

Xiaoyun Wang, Jinsong Zhang, Masafumi Nishida, Seiichi Yamamoto


Abstract
This paper describes a method of generating a reduced phoneme set for dialogue-based computer assisted language learning (CALL)systems. We designed a reduced phoneme set consisting of classified phonemes more aligned with the learners’ speech characteristics than the canonical set of a target language. This reduced phoneme set provides an inherently more appropriate model for dealing with mispronunciation by second language speakers. In this study, we used a phonetic decision tree (PDT)-based top-down sequential splitting method to generate the reduced phoneme set and then applied this method to a translation-game type English CALL system for Japanese to determine its effectiveness. Experimental results showed that the proposed method improves the performance of recognizing non-native speech.
Anthology ID:
L14-1008
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3948–3951
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/101_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Xiaoyun Wang, Jinsong Zhang, Masafumi Nishida, and Seiichi Yamamoto. 2014. Phoneme Set Design Using English Speech Database by Japanese for Dialogue-Based English CALL Systems. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3948–3951, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
Phoneme Set Design Using English Speech Database by Japanese for Dialogue-Based English CALL Systems (Wang et al., LREC 2014)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/101_Paper.pdf