The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Sistem CALL (Computer Assisted Language Learning) menggunakan ASR (Automatic Speech Recognition) untuk pembelajaran bahasa kedua telah mendapat minat yang semakin meningkat baru-baru ini. Walau bagaimanapun, ia masih menjadi cabaran untuk mencapai prestasi pengecaman pertuturan yang tinggi, termasuk pengesanan tepat sebutan yang salah oleh bukan penutur asli. Secara konvensional, pola ralat yang mungkin, berdasarkan pengetahuan linguistik, ditambahkan pada leksikon dan model bahasa, atau rangkaian tatabahasa ASR. Walau bagaimanapun, pendekatan ini mudah jatuh dalam pertukaran liputan kesilapan dan peningkatan kebingungan. Untuk menyelesaikan masalah, kami mencadangkan kaedah berdasarkan pepohon keputusan untuk mempelajari ramalan berkesan kesilapan yang dibuat oleh penutur bukan asli. Penilaian percubaan dengan sebilangan pelajar asing yang belajar bahasa Jepun menunjukkan bahawa kaedah yang dicadangkan boleh menjana rangkaian tatabahasa ASR dengan berkesan, diberikan ayat sasaran, untuk mencapai kedua-dua liputan ralat yang lebih baik dan kekeliruan yang lebih kecil, menghasilkan peningkatan ketara dalam ketepatan ASR.
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Salinan
Hongcui WANG, Tatsuya KAWAHARA, "Effective Prediction of Errors by Non-native Speakers Using Decision Tree for Speech Recognition-Based CALL System" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 12, pp. 2462-2468, December 2009, doi: 10.1587/transinf.E92.D.2462.
Abstract: CALL (Computer Assisted Language Learning) systems using ASR (Automatic Speech Recognition) for second language learning have received increasing interest recently. However, it still remains a challenge to achieve high speech recognition performance, including accurate detection of erroneous utterances by non-native speakers. Conventionally, possible error patterns, based on linguistic knowledge, are added to the lexicon and language model, or the ASR grammar network. However, this approach easily falls in the trade-off of coverage of errors and the increase of perplexity. To solve the problem, we propose a method based on a decision tree to learn effective prediction of errors made by non-native speakers. An experimental evaluation with a number of foreign students learning Japanese shows that the proposed method can effectively generate an ASR grammar network, given a target sentence, to achieve both better coverage of errors and smaller perplexity, resulting in significant improvement in ASR accuracy.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.2462/_p
Salinan
@ARTICLE{e92-d_12_2462,
author={Hongcui WANG, Tatsuya KAWAHARA, },
journal={IEICE TRANSACTIONS on Information},
title={Effective Prediction of Errors by Non-native Speakers Using Decision Tree for Speech Recognition-Based CALL System},
year={2009},
volume={E92-D},
number={12},
pages={2462-2468},
abstract={CALL (Computer Assisted Language Learning) systems using ASR (Automatic Speech Recognition) for second language learning have received increasing interest recently. However, it still remains a challenge to achieve high speech recognition performance, including accurate detection of erroneous utterances by non-native speakers. Conventionally, possible error patterns, based on linguistic knowledge, are added to the lexicon and language model, or the ASR grammar network. However, this approach easily falls in the trade-off of coverage of errors and the increase of perplexity. To solve the problem, we propose a method based on a decision tree to learn effective prediction of errors made by non-native speakers. An experimental evaluation with a number of foreign students learning Japanese shows that the proposed method can effectively generate an ASR grammar network, given a target sentence, to achieve both better coverage of errors and smaller perplexity, resulting in significant improvement in ASR accuracy.},
keywords={},
doi={10.1587/transinf.E92.D.2462},
ISSN={1745-1361},
month={December},}
Salinan
TY - JOUR
TI - Effective Prediction of Errors by Non-native Speakers Using Decision Tree for Speech Recognition-Based CALL System
T2 - IEICE TRANSACTIONS on Information
SP - 2462
EP - 2468
AU - Hongcui WANG
AU - Tatsuya KAWAHARA
PY - 2009
DO - 10.1587/transinf.E92.D.2462
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2009
AB - CALL (Computer Assisted Language Learning) systems using ASR (Automatic Speech Recognition) for second language learning have received increasing interest recently. However, it still remains a challenge to achieve high speech recognition performance, including accurate detection of erroneous utterances by non-native speakers. Conventionally, possible error patterns, based on linguistic knowledge, are added to the lexicon and language model, or the ASR grammar network. However, this approach easily falls in the trade-off of coverage of errors and the increase of perplexity. To solve the problem, we propose a method based on a decision tree to learn effective prediction of errors made by non-native speakers. An experimental evaluation with a number of foreign students learning Japanese shows that the proposed method can effectively generate an ASR grammar network, given a target sentence, to achieve both better coverage of errors and smaller perplexity, resulting in significant improvement in ASR accuracy.
ER -