{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:38:19Z","timestamp":1750307899174,"version":"3.41.0"},"reference-count":29,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2008,2,1]],"date-time":"2008-02-01T00:00:00Z","timestamp":1201824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Transactions on Asian Language Information Processing"],"published-print":{"date-parts":[[2008,2]]},"abstract":"The main issues of practical spoken-language applications for\nhuman-computer interface are how to overcome speech recognition\nerrors and guarantee the reasonable end-performance of\nspoken-language applications. Therefore, handling the erroneously\nrecognized outputs is a key in developing robust spoken-language\nsystems. To address this problem, we present a method to improve\nthe accuracy of speech recognition and performance of\nspoken-language applications. The proposed error corrective\nreranking approach exploits recognition environment characteristics\nand domain-specific semantic information to provide robustness and\nadaptability for a spoken-language system. We demonstrate some\nexperiments of spoken dialogue tasks and empirical results that\nshow an improvement in accuracy for both speech recognition and\nspoken-language understanding. In our experiment, we show an error\nreduction of up to 9.7% and 16.8%; of word error rate, and 5.5% and\n7.9% of understanding error for the air travel and telebanking\nservice domains.<\/jats:p>","DOI":"10.1145\/1330291.1330293","type":"journal-article","created":{"date-parts":[[2008,2,8]],"date-time":"2008-02-08T15:32:16Z","timestamp":1202484736000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving Speech Recognition and Understanding using Error-Corrective Reranking"],"prefix":"10.1145","volume":"7","author":[{"given":"Minwoo","family":"Jeong","sequence":"first","affiliation":[{"name":"Pohang University of Science and Technology"}]},{"given":"Gary Geunbae","family":"Lee","sequence":"additional","affiliation":[{"name":"Pohang University of Science and Technology"}]}],"member":"320","published-online":{"date-parts":[[2008,2,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Barnett J. Anderson S. Broglio J. Singh M. Hudson R. and Kuo S. 1997. Experiments in spoken queries for documents retrieval. In <it>Proceedings of the Eurospeech<\/it>. Vol. 3. Barnett J. Anderson S. Broglio J. Singh M. Hudson R. and Kuo S. 1997. Experiments in spoken queries for documents retrieval. In <it>Proceedings of the Eurospeech<\/it>. Vol. 3.","DOI":"10.21437\/Eurospeech.1997-371"},{"key":"e_1_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Bellegarda J. R. 2004. Statistical language model adaptation: Review and perspectives. <it>Speech Comm. 42 <\/it> 1 93--108. Bellegarda J. R. 2004. Statistical language model adaptation: Review and perspectives. <it>Speech Comm. 42 <\/it> 1 93--108.","DOI":"10.1016\/j.specom.2003.08.002"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3115\/1075218.1075255"},{"key":"e_1_2_1_4_1","first-page":"263","article-title":"The mathematics of statistical machine translation: Parameter estimation. <it>Computat","volume":"2","author":"Brown P. F.","year":"1994","journal-title":"Linguist. 19,<\/it>"},{"key":"e_1_2_1_5_1","first-page":"467","article-title":"Class-based n-gram models of natural language. <it>Computat","volume":"4","author":"Brown P. F.","year":"1992","journal-title":"Linguist. 18,<\/it>"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1006\/csla.1999.0128"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/89.817452"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022673506211"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1162\/0891201053630273"},{"key":"e_1_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Crestani F. 2000. Word recognition errors and relevance feedback in spoken query processing. In <it>Proceedings of the Flexible Query Answering Systems Conference<\/it>. Crestani F. 2000. Word recognition errors and relevance feedback in spoken query processing. In <it>Proceedings of the Flexible Query Answering Systems Conference<\/it>.","DOI":"10.1007\/978-3-7908-1834-5_25"},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Eun J. Jeong M. and Lee G. G. 2005. A multiple classifier-based concept-spotting approach for robust spoken-language understanding. In <it>Proceedings of the Interspeech Eurospeech<\/it>. Lisbon Portugal 3441--3444. Eun J. Jeong M. and Lee G. G. 2005. A multiple classifier-based concept-spotting approach for robust spoken-language understanding. In <it>Proceedings of the Interspeech Eurospeech<\/it>. Lisbon Portugal 3441--3444.","DOI":"10.21437\/Interspeech.2005-308"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Fujii A. Itou K. and Ishikawa T. 2002. Speech-driven text retrieval: Using target IR collections for statistical language model adaptation in speech recognition. A. R. Coden and E. W. Brown and S. Srinivasan (Eds.) <it>Information Retrieval Techniques for Speech Application<\/it> Lecture Notes in Computer Science vol. 2273. Fujii A. Itou K. and Ishikawa T. 2002. Speech-driven text retrieval: Using target IR collections for statistical language model adaptation in speech recognition. A. R. Coden and E. W. Brown and S. Srinivasan (Eds.) <it>Information Retrieval Techniques for Speech Application<\/it> Lecture Notes in Computer Science vol. 2273.","DOI":"10.1007\/3-540-45637-6_9"},{"key":"e_1_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Good I. J. 1953. The population frequencies of species and the estimation of population parameters. <it>Biometrika 40 <\/it> 3-4 237--264. Good I. J. 1953. The population frequencies of species and the estimation of population parameters. <it>Biometrika 40 <\/it> 3-4 237--264.","DOI":"10.1093\/biomet\/40.3-4.237"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.3115\/1072228.1072397"},{"key":"e_1_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Jeong M. Eun J. Jung S. and Lee G. G. 2005. An error-corrective language-model adaptation for automatic speech recognition. In <it>Proceedings of the Interspeech Eurospeech<\/it>. Lisbon Portugal 729--732. Jeong M. Eun J. Jung S. and Lee G. G. 2005. An error-corrective language-model adaptation for automatic speech recognition. In <it>Proceedings of the Interspeech Eurospeech<\/it>. Lisbon Portugal 729--732.","DOI":"10.21437\/Interspeech.2005-12"},{"key":"e_1_2_1_16_1","unstructured":"Jeong M. Kim B. and Lee G. G. 2004. Using higher-level linguistic knowledge for speech recognition error correction in a spoken Q\/A dialog. In <it>Proceedings of HLT-NAACL Workshop on Spoken Language Understanding for Conversational Systems and Higher Level Linguistic Information for Speech Processing<\/it>. 66--69. Jeong M. Kim B. and Lee G. G. 2004. Using higher-level linguistic knowledge for speech recognition error correction in a spoken Q\/A dialog. In <it>Proceedings of HLT-NAACL Workshop on Spoken Language Understanding for Conversational Systems and Higher Level Linguistic Information for Speech Processing<\/it>. 66--69."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.3115\/980845.980954"},{"key":"e_1_2_1_18_1","first-page":"355","article-title":"Maximum entropy techniques for exploiting syntactic, semantic and collocational dependencies in language modeling. <it>Comput","volume":"4","author":"Khudanpur S.","year":"2000","journal-title":"Speech Lang. 14,<\/it>"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.3115\/1073445.1073462"},{"key":"e_1_2_1_20_1","unstructured":"Lafferty J. McCallum A. and Pereira F. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In <it>Proceedings of the International Conference on Machine Learning (ICML)<\/it>. 282--289. Lafferty J. McCallum A. and Pereira F. 2001. 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