{"id":"https://openalex.org/W1534006680","doi":"https://doi.org/10.21437/icslp.2002-379","title":"Using EM-trained string-edit distances for approximate matching of acoustic morphemes","display_name":"Using EM-trained string-edit distances for approximate matching of acoustic morphemes","publication_year":2002,"publication_date":"2002-09-16","ids":{"openalex":"https://openalex.org/W1534006680","doi":"https://doi.org/10.21437/icslp.2002-379","mag":"1534006680"},"language":"en","primary_location":{"id":"doi:10.21437/icslp.2002-379","is_oa":false,"landing_page_url":"https://doi.org/10.21437/icslp.2002-379","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"7th International Conference on Spoken Language Processing (ICSLP 2002)","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/A5011053835","display_name":"Michael Levit","orcid":null},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael Levit","raw_affiliation_strings":["University of Erlangen-Nuremberg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Erlangen-Nuremberg","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054443473","display_name":"Elmar N\u00f6th","orcid":"https://orcid.org/0000-0002-3396-555X"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Elmar N\u00f6th","raw_affiliation_strings":["University of Erlangen-Nuremberg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Erlangen-Nuremberg","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051646883","display_name":"Allen L. Gorin","orcid":null},"institutions":[{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Allen Gorin","raw_affiliation_strings":["At&T#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"At&T#TAB#","institution_ids":["https://openalex.org/I1283103587"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5615,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.59882141,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1157","last_page":"1160"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"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":0.9998000264167786,"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/T12031","display_name":"Speech and dialogue systems","score":0.9990000128746033,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.996999979019165,"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/morpheme","display_name":"Morpheme","score":0.8092175126075745},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7683652639389038},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6600679159164429},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6495881676673889},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.5595786571502686},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.549299955368042},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5352110862731934},{"id":"https://openalex.org/keywords/phone","display_name":"Phone","score":0.5131729245185852},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.48266294598579407},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.43483203649520874},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.42978739738464355},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.42443740367889404},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3327583074569702},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.1659722924232483},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10945561528205872},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.07703739404678345}],"concepts":[{"id":"https://openalex.org/C165297611","wikidata":"https://www.wikidata.org/wiki/Q43249","display_name":"Morpheme","level":2,"score":0.8092175126075745},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7683652639389038},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6600679159164429},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6495881676673889},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.5595786571502686},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.549299955368042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5352110862731934},{"id":"https://openalex.org/C2778707766","wikidata":"https://www.wikidata.org/wiki/Q202064","display_name":"Phone","level":2,"score":0.5131729245185852},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.48266294598579407},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.43483203649520874},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.42978739738464355},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.42443740367889404},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3327583074569702},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.1659722924232483},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10945561528205872},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.07703739404678345},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/icslp.2002-379","is_oa":false,"landing_page_url":"https://doi.org/10.21437/icslp.2002-379","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"7th International Conference on Spoken Language Processing (ICSLP 2002)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.5.3653","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5.3653","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www5.informatik.uni-erlangen.de/literature/ps-dir/2002/Levit02:UES.ps.gz","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W56967673","https://openalex.org/W1505906253","https://openalex.org/W1552962923","https://openalex.org/W2049633694","https://openalex.org/W2171144711"],"related_works":["https://openalex.org/W2061937230","https://openalex.org/W1532139253","https://openalex.org/W2189081180","https://openalex.org/W2594897229","https://openalex.org/W2151348424","https://openalex.org/W4221142855","https://openalex.org/W2050138804","https://openalex.org/W2129812225","https://openalex.org/W4290708361","https://openalex.org/W26527944"],"abstract_inverted_index":{"Our":[0],"research":[1],"concerns":[2],"spoken":[3],"language":[4,26],"understanding":[5,31],"within":[6],"the":[7,14,42,55,79,82,97,112],"domain":[8],"of":[9,24,32,37,44,50,69],"automated":[10],"telecommunication":[11],"services.":[12],"In":[13],"recent":[15],"papers":[16],"we":[17,61,100],"presented":[18],"a":[19,45],"new":[20],"methodology":[21],"for":[22,28,121],"training":[23],"statistical":[25],"models":[27],"recognition":[29],"and":[30],"utterances":[33],"from":[34],"large":[35,67],"corpora":[36],"phone":[38],"sequences":[39],"obtained":[40],"as":[41],"output":[43],"task-independent":[46],"ASR-system.":[47],"The":[48,117],"advantage":[49],"this":[51,103],"strategy":[52,58,84],"compared":[53],"to":[54,64,73,77,114],"traditional":[56],"word-based":[57],"is":[59],"that":[60],"don&amp;apos;t":[62],"have":[63],"manually":[65],"transcribe":[66],"amounts":[68],"data":[70],"in":[71,96,102,111],"order":[72],"extract":[74],"acoustic":[75,94],"morphemes":[76,95],"train":[78],"classifier.":[80],"Since":[81],"baseline":[83],"suffered":[85],"high":[86],"False":[87],"Rejection":[88],"Rates":[89],"caused":[90],"by":[91],"finding":[92],"no":[93],"test":[98],"data,":[99],"describe":[101],"paper":[104],"how":[105],"approximate":[106],"matching":[107],"can":[108],"be":[109],"incorporated":[110],"Bayes-classifier":[113],"reduce":[115],"FRR.":[116],"experiments":[118],"are":[119],"evaluated":[120],"&amp;quot;How":[122],"May":[123],"I":[124],"Help":[125],"You?&amp;quot;-task.":[126]},"counts_by_year":[{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
