Phonetic confusion matrix based spoken document retrieval Export

@inproceedings{citeulike:4301686, abstract = {Combined word-based index and phonetic indexes have been used to improve the performance of spoken document retrieval systems primarily by addressing the out-of-vocabulary retrieval problem. However, a known problem with phonetic recognition is its limited accuracy in comparison with word level recognition. We propose a novel method for phonetic retrieval in the CueVideo system based on the probabilistic formulation of term weighting using phone confusion data in a Bayesian framework. We evaluate this method of spoken document retrieval against word-based retrieval for the search levels identified in a realistic video-based distributed learning setting. Using our test data, we achieved an average recall of 0.88 with an average precision of 0.69 for retrieval of out-of-vocabulary words on phonetic transcripts with 35\% word error rate. For in-vocabulary words, we achieved a 17\% improvement in recall over word-based retrieval with a 17\% loss in precision for word error rites ranging from 35 to 65\%.}, address = {New York, NY, USA}, author = {Srinivasan, Savitha and Petkovic, Dragutin}, booktitle = {SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval}, citeulike-article-id = {4301686}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=345508.345552}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/345508.345552}, doi = {10.1145/345508.345552}, isbn = {1-58113-226-3}, keywords = {confusion, phone}, location = {Athens, Greece}, pages = {81--87}, posted-at = {2009-04-11 17:47:15}, priority = {0}, publisher = {ACM}, title = {Phonetic confusion matrix based spoken document retrieval}, url = {http://dx.doi.org/10.1145/345508.345552}, year = {2000} }

TY - CONF ID - citeulike:4301686 L3 - citeulike-article-id:4301686 N2 - Combined word-based index and phonetic indexes have been used to improve the performance of spoken document retrieval systems primarily by addressing the out-of-vocabulary retrieval problem. However, a known problem with phonetic recognition is its limited accuracy in comparison with word level recognition. We propose a novel method for phonetic retrieval in the CueVideo system based on the probabilistic formulation of term weighting using phone confusion data in a Bayesian framework. We evaluate this method of spoken document retrieval against word-based retrieval for the search levels identified in a realistic video-based distributed learning setting. Using our test data, we achieved an average recall of 0.88 with an average precision of 0.69 for retrieval of out-of-vocabulary words on phonetic transcripts with 35% word error rate. For in-vocabulary words, we achieved a 17% improvement in recall over word-based retrieval with a 17% loss in precision for word error rites ranging from 35 to 65%. CY - Athens, Greece SN - 1-58113-226-3 AD - New York, NY, USA EP - 87 TI - Phonetic confusion matrix based spoken document retrieval PB - ACM T2 - SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval SP - 81 KW - confusion KW - phone AU - Srinivasan, Savitha AU - Petkovic, Dragutin PY - 2000/// UR - http://dx.doi.org/10.1145/345508.345552 ER -