{"id":"https://openalex.org/W4226158189","doi":"https://doi.org/10.1109/icassp43922.2022.9746480","title":"A Likelihood Ratio Based Domain Adaptation Method for E2E Models","display_name":"A Likelihood Ratio Based Domain Adaptation Method for E2E Models","publication_year":2022,"publication_date":"2022-04-27","ids":{"openalex":"https://openalex.org/W4226158189","doi":"https://doi.org/10.1109/icassp43922.2022.9746480"},"language":"en","primary_location":{"id":"doi:10.1109/icassp43922.2022.9746480","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9746480","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5089128240","display_name":"Chhavi Choudhury","orcid":null},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chhavi Choudhury","raw_affiliation_strings":["Amazon Alexa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon Alexa","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014054572","display_name":"Ankur Gandhe","orcid":null},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ankur Gandhe","raw_affiliation_strings":["Amazon Alexa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon Alexa","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101762391","display_name":"Xiaohan Ding","orcid":"https://orcid.org/0009-0003-2679-3344"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaohan Ding","raw_affiliation_strings":["Amazon Alexa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon Alexa","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109934165","display_name":"Ivan Bulyko","orcid":null},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ivan Bulyko","raw_affiliation_strings":["Amazon Alexa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon Alexa","institution_ids":["https://openalex.org/I1311688040"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1311688040"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9944999814033508,"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":0.9926999807357788,"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/computer-science","display_name":"Computer science","score":0.8176945447921753},{"id":"https://openalex.org/keywords/oracle","display_name":"Oracle","score":0.6968104243278503},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.6389859914779663},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5915133357048035},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5773420333862305},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.551561713218689},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5495737791061401},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.49805450439453125},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4940229058265686},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.48297110199928284},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.44880443811416626},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.4311743378639221},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42970022559165955},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.35655277967453003},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.29279690980911255}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8176945447921753},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.6968104243278503},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.6389859914779663},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5915133357048035},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5773420333862305},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.551561713218689},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5495737791061401},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.49805450439453125},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4940229058265686},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.48297110199928284},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.44880443811416626},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.4311743378639221},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42970022559165955},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35655277967453003},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29279690980911255},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp43922.2022.9746480","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9746480","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.7900000214576721}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1828163288","https://openalex.org/W2143612262","https://openalex.org/W2160815625","https://openalex.org/W2577366047","https://openalex.org/W2797259383","https://openalex.org/W2799800213","https://openalex.org/W2903732155","https://openalex.org/W2962760690","https://openalex.org/W2962765220","https://openalex.org/W2962826786","https://openalex.org/W2963240019","https://openalex.org/W2963827914","https://openalex.org/W2972625221","https://openalex.org/W3008037978","https://openalex.org/W3008181812","https://openalex.org/W3094667432","https://openalex.org/W3094979069","https://openalex.org/W3096815019","https://openalex.org/W3152221657","https://openalex.org/W3163462603","https://openalex.org/W6638749077","https://openalex.org/W6732447497","https://openalex.org/W6746574493"],"related_works":["https://openalex.org/W3035557009","https://openalex.org/W2955172689","https://openalex.org/W2341113105","https://openalex.org/W3204418343","https://openalex.org/W3132602785","https://openalex.org/W3046182208","https://openalex.org/W2186589590","https://openalex.org/W4297818280","https://openalex.org/W2343346879","https://openalex.org/W772479628"],"abstract_inverted_index":{"End-to-end":[0],"(E2E)":[1],"automatic":[2],"speech":[3],"recognition":[4],"models":[5,26,52],"like":[6,21],"Recurrent":[7],"Neural":[8],"Networks":[9],"Transducer":[10],"(RNN-T)":[11],"are":[12,27,38,60,64],"becoming":[13],"a":[14,47,81,116,145,160],"popular":[15],"choice":[16],"for":[17],"streaming":[18],"ASR":[19],"applications":[20],"voice":[22],"assistants.":[23],"While":[24],"E2E":[25],"very":[28],"effective":[29,107],"at":[30],"learning":[31],"representation":[32],"of":[33,73,119,159],"the":[34,69,153],"training":[35,58],"data":[36,90],"they":[37],"trained":[39],"on,":[40],"their":[41],"accuracy":[42],"on":[43,137,144],"unseen":[44],"domains":[45,98],"remains":[46],"challenging":[48],"problem.":[49],"Additionally,":[50],"these":[51],"require":[53],"paired":[54],"audio":[55],"and":[56,63,99,113,127],"text":[57,89],"data,":[59],"computationally":[61],"expensive":[62],"difficult":[65],"to":[66,92,96],"adapt":[67,93],"towards":[68],"fast":[70],"evolving":[71],"nature":[72],"conversational":[74],"speech.":[75],"In":[76],"this":[77,104],"work,":[78],"we":[79],"explore":[80],"contextual":[82,154],"biasing":[83,155],"approach":[84],"using":[85],"likelihood-ratio":[86],"that":[87,103,151],"leverages":[88],"sources":[91],"RNN-T":[94],"model":[95,163],"new":[97],"entities.":[100],"We":[101,148],"show":[102,150],"method":[105],"is":[106],"in":[108,115,121,129],"improving":[109],"rare":[110],"words":[111],"recognition,":[112],"results":[114],"relative":[117],"improvement":[118],"10%":[120,128],"1-best":[122],"word":[123],"error":[124],"rate":[125],"(WER)":[126],"n-best":[130],"Oracle":[131],"<sup":[132],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[133],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[134],"WER":[135,166],"(n=8)":[136],"multiple":[138],"out-of-domain":[139],"datasets":[140],"without":[141],"any":[142],"degradation":[143],"general":[146],"dataset.":[147],"also":[149],"complementing":[152],"adaptation":[156,158],"with":[157],"second-pass":[161],"rescoring":[162],"gives":[164],"additive":[165],"improvements.":[167]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
