{"id":"https://openalex.org/W2295262519","doi":"https://doi.org/10.21437/interspeech.2015-699","title":"Discriminative data selection for lightly supervised training of acoustic model using closed caption texts","display_name":"Discriminative data selection for lightly supervised training of acoustic model using closed caption texts","publication_year":2015,"publication_date":"2015-09-06","ids":{"openalex":"https://openalex.org/W2295262519","doi":"https://doi.org/10.21437/interspeech.2015-699","mag":"2295262519"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2015-699","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2015-699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2015","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/A5053726259","display_name":"Sheng Li","orcid":"https://orcid.org/0000-0001-7636-3797"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sheng Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066545740","display_name":"Yuya Akita","orcid":"https://orcid.org/0009-0003-3036-0883"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuya Akita","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5038044080","display_name":"Tatsuya Kawahara","orcid":"https://orcid.org/0000-0002-2686-2296"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tatsuya Kawahara","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3526","last_page":"3530"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9997000098228455,"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":0.9997000098228455,"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/T11309","display_name":"Music and Audio Processing","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9991000294685364,"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/discriminative-model","display_name":"Discriminative model","score":0.8165221214294434},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7869357466697693},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.581275224685669},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5767321586608887},{"id":"https://openalex.org/keywords/usable","display_name":"USable","score":0.5354794263839722},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5020651817321777},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.478171169757843},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.46719691157341003},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4526670575141907},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4478703737258911},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44610682129859924},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.42759978771209717},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.41162100434303284},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37656378746032715},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10400596261024475},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09287238121032715}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8165221214294434},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7869357466697693},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.581275224685669},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5767321586608887},{"id":"https://openalex.org/C2780615836","wikidata":"https://www.wikidata.org/wiki/Q2471869","display_name":"USable","level":2,"score":0.5354794263839722},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5020651817321777},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.478171169757843},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.46719691157341003},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4526670575141907},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4478703737258911},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44610682129859924},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.42759978771209717},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41162100434303284},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37656378746032715},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10400596261024475},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09287238121032715},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"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/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/interspeech.2015-699","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2015-699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2015","raw_type":"proceedings-article"},{"id":"pmh:oai:t2r2.star.titech.ac.jp:50728201","is_oa":false,"landing_page_url":"http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100930685","pdf_url":null,"source":{"id":"https://openalex.org/S4377196385","display_name":"Tokyo Tech Research Repository (Tokyo Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114531698","host_organization_name":"Tokyo Institute of Technology","host_organization_lineage":["https://openalex.org/I114531698"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W65139299","https://openalex.org/W72347498","https://openalex.org/W201749652","https://openalex.org/W1480268964","https://openalex.org/W1505175215","https://openalex.org/W1524333225","https://openalex.org/W1579799841","https://openalex.org/W1975113979","https://openalex.org/W2047691586","https://openalex.org/W2105093271","https://openalex.org/W2114168002","https://openalex.org/W2133869761","https://openalex.org/W2140914930","https://openalex.org/W2143577772","https://openalex.org/W2147880316","https://openalex.org/W2151826411","https://openalex.org/W2159748844","https://openalex.org/W2160986985","https://openalex.org/W2169384404","https://openalex.org/W2917604489","https://openalex.org/W3034729383","https://openalex.org/W3208649718"],"related_works":["https://openalex.org/W3172695526","https://openalex.org/W1757117718","https://openalex.org/W2889166412","https://openalex.org/W2130553454","https://openalex.org/W3022007134","https://openalex.org/W4317548404","https://openalex.org/W3104108945","https://openalex.org/W2033364610","https://openalex.org/W3163689946","https://openalex.org/W2797776314"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,16,33,53],"novel":[3],"data":[4,20,83],"selection":[5],"method":[6,113],"for":[7,84],"lightly":[8,115],"supervised":[9,116],"training":[10,87,117],"of":[11,19,35,42,55,114],"acoustic":[12,85],"model,":[13],"which":[14],"exploits":[15],"large":[17],"amount":[18],"with":[21,110],"closed":[22,37],"caption":[23,38],"texts":[24],"but":[25],"not":[26],"faithful":[27],"transcripts.":[28],"In":[29],"the":[30,36,43,47,64,76,81,97,103,111],"proposed":[31],"scheme,":[32],"sequence":[34],"text":[39],"and":[40,60,106,122],"that":[41,75],"ASR":[44,98],"hypothesis":[45],"by":[46],"baseline":[48,104],"system":[49,105],"are":[50],"aligned.":[51],"Then,":[52],"set":[54],"dedicated":[56],"classifiers":[57,77],"is":[58,73,100],"designed":[59],"trained":[61],"to":[62],"select":[63],"correct":[65],"one":[66],"among":[67],"them":[68],"or":[69],"reject":[70],"both.":[71],"It":[72],"demonstrated":[74],"can":[78],"effectively":[79],"filter":[80],"usable":[82],"model":[86],"without":[88],"tuning":[89],"any":[90],"threshold":[91],"parameters.":[92],"A":[93],"significant":[94],"improvement":[95],"in":[96,108],"accuracy":[99],"achieved":[101],"from":[102],"also":[107],"comparison":[109],"conventional":[112],"based":[118],"on":[119],"simple":[120],"matching":[121],"confidence":[123],"measure":[124],"scores.":[125]},"counts_by_year":[{"year":2019,"cited_by_count":2},{"year":2016,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
