{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T11:46:23Z","timestamp":1648640783324},"reference-count":3,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2016,6]]},"abstract":"<jats:p> With the rapid evolution of smart home environment, the demand for spoken information retrieval (e.g., voice-activated FAQ retrieval) on information appliances is increasing. In spoken information retrieval, users\u2019 spoken queries are converted into text queries using automatic speech recognition (ASR) engines. If top-1 results of the ASR engines are incorrect, the errors are propagated to information retrieval systems. If a document collection is a small set of sentences such as frequently asked questions (FAQs), the errors have additional effect on the performance of information retrieval systems. To improve the performance of such a sentence retrieval system, we propose a post-processing model of an ASR engine. The post-processing model consists of a re-ranking and a query term generation model. The re-ranking model rearranges top-n outputs of the ASR engines using the ranking support vector machine (Ranking SVM). The query term generation model extracts meaningful content words from the re-ranked queries based on term frequencies and query rankings. In the experiments, the re-ranking model improved the top-1 performance results of an underlying ASR engine with 4.4% higher precision and 6.4% higher recall rate. The query term generation model improved the performance results of an underlying information retrieval system with an accuracy 2.4% to 2.6% higher. Based on the experimental result, the proposed model revealed that it could improve the performance of a spoken sentence retrieval system in a restricted domain. <\/jats:p>","DOI":"10.1142\/s0218213016500172","type":"journal-article","created":{"date-parts":[[2016,4,12]],"date-time":"2016-04-12T22:58:55Z","timestamp":1460501935000},"page":"1650017","source":"Crossref","is-referenced-by-count":2,"title":["Enhanced Spoken Sentence Retrieval Using a Conventional Automatic Speech Recognizer in Smart Home"],"prefix":"10.1142","volume":"25","author":[{"given":"Hyeokju","family":"Ahn","sequence":"first","affiliation":[{"name":"Program of Computer and Communications Engineering, Kangwon National University, 1 Gangwondaehak-gil, Chuncheon-si, Gangwon-do, 200-701, Republic of Korea"}]},{"given":"Harksoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Program of Computer and Communications Engineering, Kangwon National University, 1 Gangwondaehak-gil, Chuncheon-si, Gangwon-do, 200-701, Republic of Korea"}]}],"member":"219","published-online":{"date-parts":[[2016,6,28]]},"reference":[{"issue":"2","key":"p_3","first-page":"1","volume":"4","author":"Chandak M. B.","year":"2010","journal-title":"International Journal of Smart Home"},{"key":"p_4","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2008.23"},{"key":"p_6","first-page":"6064957","author":"Brandow R. L.","year":"2000","journal-title":"United States Patent"}],"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213016500172","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T22:18:16Z","timestamp":1565129896000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213016500172"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6]]},"references-count":3,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2016,6,28]]},"published-print":{"date-parts":[[2016,6]]}},"alternative-id":["10.1142\/S0218213016500172"],"URL":"https:\/\/doi.org\/10.1142\/s0218213016500172","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,6]]}}}