{"id":"https://openalex.org/W2134841146","doi":"https://doi.org/10.1109/icassp.1983.1171908","title":"Probabilistic model for the performance of speech recognition systems","display_name":"Probabilistic model for the performance of speech recognition systems","publication_year":2005,"publication_date":"2005-03-24","ids":{"openalex":"https://openalex.org/W2134841146","doi":"https://doi.org/10.1109/icassp.1983.1171908","mag":"2134841146"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.1983.1171908","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.1983.1171908","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP '83. IEEE International Conference on Acoustics, Speech, and Signal Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025786251","display_name":"A. E. Rosenberg","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"A. Rosenberg","raw_affiliation_strings":["Bell Laboratories, Murray Hill, NJ, USA","Bell Laboratories, Murray Hill, New Jersey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bell Laboratories, Murray Hill, NJ, USA","institution_ids":[]},{"raw_affiliation_string":"Bell Laboratories, Murray Hill, New Jersey","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5025786251"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":null,"first_page":"1057","last_page":"1060"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9869999885559082,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9858999848365784,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.7382527589797974},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.6274151802062988},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6020001173019409},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.586012065410614},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5743796229362488},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5443629026412964},{"id":"https://openalex.org/keywords/bernoulli-trial","display_name":"Bernoulli trial","score":0.531543493270874},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5288204550743103},{"id":"https://openalex.org/keywords/bernoulli-distribution","display_name":"Bernoulli distribution","score":0.4947013556957245},{"id":"https://openalex.org/keywords/word-recognition","display_name":"Word recognition","score":0.49357664585113525},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.48993009328842163},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4400194585323334},{"id":"https://openalex.org/keywords/confusion","display_name":"Confusion","score":0.4316330850124359},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37142449617385864},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26987865567207336},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23460423946380615},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.09857398271560669}],"concepts":[{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.7382527589797974},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.6274151802062988},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6020001173019409},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.586012065410614},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5743796229362488},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5443629026412964},{"id":"https://openalex.org/C46802686","wikidata":"https://www.wikidata.org/wiki/Q1077800","display_name":"Bernoulli trial","level":2,"score":0.531543493270874},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5288204550743103},{"id":"https://openalex.org/C27956954","wikidata":"https://www.wikidata.org/wiki/Q391371","display_name":"Bernoulli distribution","level":3,"score":0.4947013556957245},{"id":"https://openalex.org/C150856459","wikidata":"https://www.wikidata.org/wiki/Q8034367","display_name":"Word recognition","level":3,"score":0.49357664585113525},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.48993009328842163},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4400194585323334},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.4316330850124359},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37142449617385864},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26987865567207336},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23460423946380615},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.09857398271560669},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C11171543","wikidata":"https://www.wikidata.org/wiki/Q41630","display_name":"Psychoanalysis","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.1983.1171908","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.1983.1171908","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP '83. IEEE International Conference on Acoustics, Speech, and Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W596388300","https://openalex.org/W1579664581","https://openalex.org/W1905385316","https://openalex.org/W1987294319","https://openalex.org/W2010742990","https://openalex.org/W2015324614","https://openalex.org/W2057833190","https://openalex.org/W2137089646","https://openalex.org/W2753933954","https://openalex.org/W4252821048"],"related_works":["https://openalex.org/W2151706800","https://openalex.org/W4366387587","https://openalex.org/W2807744205","https://openalex.org/W4324123865","https://openalex.org/W74615440","https://openalex.org/W1996380248","https://openalex.org/W4301279896","https://openalex.org/W2050627475","https://openalex.org/W2013872935","https://openalex.org/W4306802221"],"abstract_inverted_index":{"A":[0],"probabilistic":[1],"model":[2],"is":[3,67],"developed":[4],"to":[5,49,109,134,152],"account":[6,52,176],"for":[7,57,179],"the":[8,63,100,136,167,180],"error":[9,21,73,87,154],"rate":[10,88,155],"behavior":[11,89],"of":[12,20,30,62,72,86,93,102,112,148,160,166],"isolated":[13,123],"word":[14,124],"speech":[15],"recognition":[16,39,79,125],"systems.":[17],"Two":[18],"kinds":[19,71],"are":[22,107,132,144],"examined,":[23],"confusion":[24,103],"error,":[25,41],"an":[26,42,149],"a":[27,31,43,91,119,127],"priori":[28],"characterization":[29],"recognizer":[32],"which":[33],"measures":[34],"differences":[35,53,58],"between":[36,54,59],"words,":[37,55],"and":[38,163],"rank":[40,105],"posteriori":[44],"characterization,":[45],"which,":[46],"in":[47,122],"addition":[48],"taking":[50],"into":[51],"accounts":[56],"different":[60],"tokens":[61],"same":[64],"word.":[65],"It":[66],"shown":[68],"that":[69,172],"these":[70],"can":[74,96],"be":[75,97,110],"modelled":[76],"by":[77,146],"describing":[78],"trials":[80],"as":[81,90],"Bernoulli":[82],"trials.":[83],"Good":[84],"models":[85],"function":[92],"vocabulary":[94],"size":[95],"obtained":[98,117,157],"if":[99],"distributions":[101,143,175],"or":[104],"number":[106],"considered":[108],"mixtures":[111],"binomial":[113],"distributions.":[114],"The":[115,169],"data":[116],"from":[118,158],"recent":[120],"experiment":[121],"with":[126],"large":[128],"vocabulary,":[129],"(1109":[130],"words),":[131],"used":[133],"evaluate":[135],"model.":[137],"Model":[138],"functions":[139,156],"based":[140],"on":[141],"mixture":[142,174],"fit":[145],"means":[147],"optimization":[150],"algorithm":[151],"experimental":[153,181],"each":[159],"six":[161],"talkers":[162],"three":[164],"partitions":[165],"vocabulary.":[168],"results":[170],"indicate":[171],"two-way":[173],"quite":[177],"well":[178],"performance":[182],"results.":[183]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
