{"id":"https://openalex.org/W3097239815","doi":"https://doi.org/10.21437/interspeech.2020-1787","title":"Class LM and Word Mapping for Contextual Biasing in End-to-End ASR","display_name":"Class LM and Word Mapping for Contextual Biasing in End-to-End ASR","publication_year":2020,"publication_date":"2020-10-25","ids":{"openalex":"https://openalex.org/W3097239815","doi":"https://doi.org/10.21437/interspeech.2020-1787","mag":"3097239815"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2020-1787","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2020-1787","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2020","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/A5110198056","display_name":"Rongqing Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107260","display_name":"Apple (United Kingdom)","ror":"https://ror.org/01vpeym60","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210107260"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Rongqing Huang","raw_affiliation_strings":["Apple"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Apple","institution_ids":["https://openalex.org/I4210107260"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077733105","display_name":"Ossama Abdel\u2010Hamid","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ossama Abdel-hamid","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100461519","display_name":"Xinwei Li","orcid":"https://orcid.org/0000-0003-0911-5130"},"institutions":[{"id":"https://openalex.org/I4210107260","display_name":"Apple (United Kingdom)","ror":"https://ror.org/01vpeym60","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210107260"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xinwei Li","raw_affiliation_strings":["Apple"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Apple","institution_ids":["https://openalex.org/I4210107260"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009755231","display_name":"G. Evermann","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107260","display_name":"Apple (United Kingdom)","ror":"https://ror.org/01vpeym60","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210107260"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Gunnar Evermann","raw_affiliation_strings":["Apple"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Apple","institution_ids":["https://openalex.org/I4210107260"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4348","last_page":"4351"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","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/T10181","display_name":"Natural Language Processing Techniques","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/T10028","display_name":"Topic Modeling","score":0.9995999932289124,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9995999932289124,"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/end-to-end-principle","display_name":"End-to-end principle","score":0.6526014804840088},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6513925194740295},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5920683145523071},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5346044301986694},{"id":"https://openalex.org/keywords/biasing","display_name":"Biasing","score":0.45131707191467285},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3954309821128845},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38634034991264343},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17131450772285461},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.13865026831626892},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.13139399886131287},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13002029061317444},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.053399235010147095}],"concepts":[{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.6526014804840088},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6513925194740295},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5920683145523071},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5346044301986694},{"id":"https://openalex.org/C20254490","wikidata":"https://www.wikidata.org/wiki/Q719550","display_name":"Biasing","level":3,"score":0.45131707191467285},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3954309821128845},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38634034991264343},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17131450772285461},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.13865026831626892},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.13139399886131287},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13002029061317444},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.053399235010147095}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/interspeech.2020-1787","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2020-1787","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2020","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.5099999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W648786980","https://openalex.org/W1828163288","https://openalex.org/W1855892484","https://openalex.org/W1895481600","https://openalex.org/W2183341477","https://openalex.org/W2403440562","https://openalex.org/W2405883473","https://openalex.org/W2407340526","https://openalex.org/W2525778437","https://openalex.org/W2605141709","https://openalex.org/W2750499125","https://openalex.org/W2886319145","https://openalex.org/W2903250132","https://openalex.org/W2936774411","https://openalex.org/W2937402758","https://openalex.org/W2962784628","https://openalex.org/W2962824709","https://openalex.org/W2963463964","https://openalex.org/W2963747784","https://openalex.org/W2972625221","https://openalex.org/W2972741966","https://openalex.org/W4294619417"],"related_works":["https://openalex.org/W2151749779","https://openalex.org/W2069427488","https://openalex.org/W4281694563","https://openalex.org/W3179968364","https://openalex.org/W2080696413","https://openalex.org/W1506140395","https://openalex.org/W4232799642","https://openalex.org/W2912082923","https://openalex.org/W2744827311","https://openalex.org/W2163182355"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"all-neural,":[3],"end-to-end":[4],"(E2E)":[5],"ASR":[6],"systems":[7],"gained":[8],"rapid":[9],"interest":[10],"in":[11,22],"the":[12,76,82,94,98,102,110,153,164,172,180,183],"speech":[13,17],"recognition":[14],"community.They":[15],"convert":[16],"input":[18],"to":[19,56,67,79,92,108,135,142,151,157,171],"text":[20],"units":[21],"a":[23,69,89,130],"single":[24,51],"trainable":[25],"Neural":[26],"Network":[27],"model.In":[28],"ASR,":[29],"many":[30],"utterances":[31,186],"contain":[32],"rich":[33],"named":[34,36,111,184],"entities.Such":[35],"entities":[37],"may":[38],"be":[39],"user":[40],"or":[41],"location":[42],"specific":[43],"and":[44,74,101,162],"they":[45],"are":[46],"not":[47,128],"seen":[48],"during":[49,61,85,175],"training.A":[50],"model":[52,73,126],"makes":[53],"it":[54],"inflexible":[55],"utilize":[57],"dynamic":[58],"contextual":[59],"information":[60],"inference.In":[62],"this":[63,145],"paper,":[64,146],"we":[65,147],"propose":[66,88,148],"train":[68],"context":[70,83,99],"aware":[71],"E2E":[72,125],"allow":[75],"beam":[77],"search":[78],"traverse":[80],"into":[81],"FST":[84,100],"inference.We":[86],"also":[87],"simple":[90],"method":[91],"adjust":[93],"cost":[95],"discrepancy":[96],"between":[97],"base":[103],"model.This":[104],"algorithm":[105,150,177],"is":[106],"able":[107],"reduce":[109],"entity":[112,155,185],"utterance":[113],"WER":[114,181],"by":[115,187],"57%":[116],"with":[117],"little":[118],"accuracy":[119],"degradation":[120],"on":[121,182],"regular":[122],"utterances.Although":[123],"an":[124,149,168],"does":[127],"need":[129],"pronunciation":[131,140,161],"dictionary,":[132],"its":[133],"interesting":[134],"make":[136],"use":[137],"of":[138],"existing":[139],"knowledge":[141],"improve":[143],"accuracy.In":[144],"map":[152],"rare":[154],"words":[156,159,166],"common":[158],"via":[160],"treat":[163],"mapped":[165],"as":[167],"alternative":[169],"form":[170],"original":[173],"word":[174],"recognition.This":[176],"further":[178],"reduces":[179],"another":[188],"31%.":[189]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":10}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}