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
Kami menangani isu sebutan luar tatabahasa (OOG) dalam sistem dialog pertuturan dengan menjana mesej bantuan. Bantu penjanaan mesej untuk ujaran OOG adalah satu cabaran kerana pemahaman bahasa berdasarkan pengecaman pertuturan automatik (ASR) bagi sebutan OOG biasanya salah; perkataan-perkataan penting sering disalah erti atau hilang daripada ujaran tersebut. kami pengesahan tatabahasa kaedah menggunakan transduser keadaan terhingga berwajaran, untuk mengenal pasti dengan tepat peraturan tatabahasa yang pengguna ingin gunakan untuk sebutan, walaupun jika perkataan penting tiada daripada hasil ASR. Kami kemudian menggunakan algoritma kedudukan, RankBoost, untuk menilai calon mesej bantuan mengikut urutan kemungkinan kegunaan. Ciri-cirinya termasuk hasil pengesahan tatabahasa dan sejarah ujaran yang mewakili pengalaman pengguna.
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Salinan
Kazunori KOMATANI, Yuichiro FUKUBAYASHI, Satoshi IKEDA, Tetsuya OGATA, Hiroshi G. OKUNO, "Selecting Help Messages by Using Robust Grammar Verification for Handling Out-of-Grammar Utterances in Spoken Dialogue Systems" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 12, pp. 3359-3367, December 2010, doi: 10.1587/transinf.E93.D.3359.
Abstract: We address the issue of out-of-grammar (OOG) utterances in spoken dialogue systems by generating help messages. Help message generation for OOG utterances is a challenge because language understanding based on automatic speech recognition (ASR) of OOG utterances is usually erroneous; important words are often misrecognized or missing from such utterances. Our grammar verification method uses a weighted finite-state transducer, to accurately identify the grammar rule that the user intended to use for the utterance, even if important words are missing from the ASR results. We then use a ranking algorithm, RankBoost, to rank help message candidates in order of likely usefulness. Its features include the grammar verification results and the utterance history representing the user's experience.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.3359/_p
Salinan
@ARTICLE{e93-d_12_3359,
author={Kazunori KOMATANI, Yuichiro FUKUBAYASHI, Satoshi IKEDA, Tetsuya OGATA, Hiroshi G. OKUNO, },
journal={IEICE TRANSACTIONS on Information},
title={Selecting Help Messages by Using Robust Grammar Verification for Handling Out-of-Grammar Utterances in Spoken Dialogue Systems},
year={2010},
volume={E93-D},
number={12},
pages={3359-3367},
abstract={We address the issue of out-of-grammar (OOG) utterances in spoken dialogue systems by generating help messages. Help message generation for OOG utterances is a challenge because language understanding based on automatic speech recognition (ASR) of OOG utterances is usually erroneous; important words are often misrecognized or missing from such utterances. Our grammar verification method uses a weighted finite-state transducer, to accurately identify the grammar rule that the user intended to use for the utterance, even if important words are missing from the ASR results. We then use a ranking algorithm, RankBoost, to rank help message candidates in order of likely usefulness. Its features include the grammar verification results and the utterance history representing the user's experience.},
keywords={},
doi={10.1587/transinf.E93.D.3359},
ISSN={1745-1361},
month={December},}
Salinan
TY - JOUR
TI - Selecting Help Messages by Using Robust Grammar Verification for Handling Out-of-Grammar Utterances in Spoken Dialogue Systems
T2 - IEICE TRANSACTIONS on Information
SP - 3359
EP - 3367
AU - Kazunori KOMATANI
AU - Yuichiro FUKUBAYASHI
AU - Satoshi IKEDA
AU - Tetsuya OGATA
AU - Hiroshi G. OKUNO
PY - 2010
DO - 10.1587/transinf.E93.D.3359
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2010
AB - We address the issue of out-of-grammar (OOG) utterances in spoken dialogue systems by generating help messages. Help message generation for OOG utterances is a challenge because language understanding based on automatic speech recognition (ASR) of OOG utterances is usually erroneous; important words are often misrecognized or missing from such utterances. Our grammar verification method uses a weighted finite-state transducer, to accurately identify the grammar rule that the user intended to use for the utterance, even if important words are missing from the ASR results. We then use a ranking algorithm, RankBoost, to rank help message candidates in order of likely usefulness. Its features include the grammar verification results and the utterance history representing the user's experience.
ER -