{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:22:51Z","timestamp":1775229771639,"version":"3.50.1"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2010,1,1]],"date-time":"2010-01-01T00:00:00Z","timestamp":1262304000000},"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 Trans. Inf. Syst."],"published-print":{"date-parts":[[2010,1]]},"abstract":"<jats:p>\n            Recent research efforts on spoken document retrieval have tried to overcome the low quality of 1-best automatic speech recognition transcripts, especially in the case of conversational speech, by using statistics derived from speech lattices containing multiple transcription hypotheses as output by a speech recognizer. We present a method for lattice-based spoken document retrieval based on a statistical\n            <jats:italic>n<\/jats:italic>\n            -gram modeling approach to information retrieval. In this statistical lattice-based retrieval (SLBR) method, a smoothed statistical model is estimated for each document from the expected counts of words given the information in a lattice, and the relevance of each document to a query is measured as a probability under such a model. We investigate the efficacy of our method under various parameter settings of the speech recognition and lattice processing engines, using the Fisher English Corpus of conversational telephone speech. Experimental results show that our method consistently achieves better retrieval performance than using only the 1-best transcripts in statistical retrieval, outperforms a recently proposed lattice-based vector space retrieval method, and also compares favorably with a lattice-based retrieval method based on the Okapi BM25 model.\n          <\/jats:p>","DOI":"10.1145\/1658377.1658379","type":"journal-article","created":{"date-parts":[[2010,1,26]],"date-time":"2010-01-26T14:01:38Z","timestamp":1264514498000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Statistical lattice-based spoken document retrieval"],"prefix":"10.1145","volume":"28","author":[{"given":"Tee Kiah","family":"Chia","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore"}]},{"given":"Khe Chai","family":"Sim","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, Singapore"}]},{"given":"Haizhou","family":"Li","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, Singapore"}]},{"given":"Hwee Tou","family":"Ng","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2010,1,29]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 7th Text Retrieval Conference (TREC-7). 181--190","author":"Abberley D.","unstructured":"Abberley , D. , Renals , S. , Cook , G. , and Robinson , T . 1998. Retrieval of broadcast news documents with the THISL system . In Proceedings of the 7th Text Retrieval Conference (TREC-7). 181--190 . Abberley, D., Renals, S., Cook, G., and Robinson, T. 1998. Retrieval of broadcast news documents with the THISL system. In Proceedings of the 7th Text Retrieval Conference (TREC-7). 181--190."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.3115\/1075096.1075102"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30500-2_3"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the Workshop on Interdisciplinary Approaches to Speech Indexing and Retrieval at HLT-NAACL. B. Ramabhadran and D. Oard, Eds., Association for Computational Linguistics","author":"Allauzen C.","unstructured":"Allauzen , C. , Mohri , M. , and Saraclar , M . 2004b. General indexation of weighted automata\u2014application to spoken utterance retrieval . 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