{"id":"https://openalex.org/W4307932983","doi":"https://doi.org/10.48550/arxiv.2210.17049","title":"Modular Hybrid Autoregressive Transducer","display_name":"Modular Hybrid Autoregressive Transducer","publication_year":2022,"publication_date":"2022-10-31","ids":{"openalex":"https://openalex.org/W4307932983","doi":"https://doi.org/10.48550/arxiv.2210.17049"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2210.17049","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.17049","pdf_url":"https://arxiv.org/pdf/2210.17049","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2210.17049","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101749753","display_name":"Zhong Meng","orcid":"https://orcid.org/0000-0001-7814-5929"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Meng, Zhong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027631053","display_name":"Tongzhou Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Tongzhou","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032640894","display_name":"Rohit Prabhavalkar","orcid":"https://orcid.org/0000-0001-5331-6058"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prabhavalkar, Rohit","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100433691","display_name":"Yu Zhang","orcid":"https://orcid.org/0000-0003-1100-4835"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025935520","display_name":"Gary Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Gary","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015927589","display_name":"Kartik Audhkhasi","orcid":"https://orcid.org/0000-0002-2340-1144"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Audhkhasi, Kartik","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027399854","display_name":"Jesse Emond","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Emond, Jesse","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032931723","display_name":"Trevor Strohman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Strohman, Trevor","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071715737","display_name":"Bhuvana Ramabhadran","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramabhadran, Bhuvana","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101909917","display_name":"Weilin Huang","orcid":"https://orcid.org/0000-0002-1520-4140"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, W. Ronny","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040131037","display_name":"Ehsan Variani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Variani, Ehsan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101673889","display_name":"Yinghui Huang","orcid":"https://orcid.org/0000-0003-0607-1507"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Yinghui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5103874391","display_name":"Pedro J. Moreno","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moreno, Pedro J.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":13,"corresponding_author_ids":["https://openalex.org/A5101749753"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"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/T10860","display_name":"Speech and Audio Processing","score":0.9933000206947327,"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/T11309","display_name":"Music and Audio Processing","score":0.9894999861717224,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/blank","display_name":"Blank","score":0.7298043966293335},{"id":"https://openalex.org/keywords/transducer","display_name":"Transducer","score":0.6975089311599731},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.6974694728851318},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6663880348205566},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.6641213893890381},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.619584321975708},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5442748665809631},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4450141191482544},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.41671645641326904},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3434399962425232},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33991557359695435},{"id":"https://openalex.org/keywords/acoustics","display_name":"Acoustics","score":0.33930152654647827},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1790793538093567},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13612675666809082},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09621423482894897},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07487481832504272}],"concepts":[{"id":"https://openalex.org/C2778089247","wikidata":"https://www.wikidata.org/wiki/Q368951","display_name":"Blank","level":2,"score":0.7298043966293335},{"id":"https://openalex.org/C56318395","wikidata":"https://www.wikidata.org/wiki/Q215928","display_name":"Transducer","level":2,"score":0.6975089311599731},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.6974694728851318},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6663880348205566},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6641213893890381},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.619584321975708},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5442748665809631},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4450141191482544},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.41671645641326904},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3434399962425232},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33991557359695435},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.33930152654647827},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1790793538093567},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13612675666809082},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09621423482894897},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07487481832504272},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2210.17049","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.17049","pdf_url":"https://arxiv.org/pdf/2210.17049","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},{"id":"doi:10.48550/arxiv.2210.17049","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2210.17049","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2210.17049","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.17049","pdf_url":"https://arxiv.org/pdf/2210.17049","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4307932983.pdf","grobid_xml":"https://content.openalex.org/works/W4307932983.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2361638505","https://openalex.org/W1993662208","https://openalex.org/W2370352440","https://openalex.org/W2009954581","https://openalex.org/W4296141694","https://openalex.org/W2379220204","https://openalex.org/W3160305016","https://openalex.org/W2005071119","https://openalex.org/W3158546193","https://openalex.org/W4249926107"],"abstract_inverted_index":{"Text-only":[0],"adaptation":[1,114],"of":[2,115,144],"a":[3,33,56,91,101,118,133],"transducer":[4,14,37],"model":[5,20,23],"remains":[6],"challenging":[7],"for":[8],"end-to-end":[9],"speech":[10],"recognition":[11],"since":[12],"the":[13],"has":[15,40],"no":[16],"clearly":[17],"separated":[18,42],"acoustic":[19,58],"(AM),":[21],"language":[22],"(LM)":[24],"or":[25],"blank":[26,45,51],"model.":[27],"In":[28],"this":[29],"work,":[30],"we":[31],"propose":[32],"modular":[34],"hybrid":[35],"autoregressive":[36],"(MHAT)":[38],"that":[39,96,105],"structurally":[41],"label":[43,49,63,80],"and":[44,50,62,71,75,90,151],"decoders":[46],"to":[47,69,78,94,110,146],"predict":[48],"distributions,":[52],"respectively,":[53],"along":[54],"with":[55,85,137,153],"shared":[57],"encoder.":[59],"The":[60],"encoder":[61],"decoder":[64],"outputs":[65],"are":[66],"directly":[67],"projected":[68],"AM":[70],"internal":[72,87,98,124],"LM":[73,88,99,104,121,125,149,154],"scores":[74],"then":[76],"added":[77],"compute":[79],"posteriors.":[81],"We":[82],"train":[83],"MHAT":[84,116,135],"an":[86],"loss":[89,93],"HAT":[92],"ensure":[95],"its":[97],"becomes":[100],"standalone":[102],"neural":[103],"can":[106],"be":[107],"effectively":[108],"adapted":[109,136],"text.":[111],"Moreover,":[112],"text":[113],"fosters":[117],"much":[119],"better":[120],"fusion":[122,150,155],"than":[123],"subtraction-based":[126],"methods.":[127],"On":[128],"Google's":[129],"large-scale":[130],"production":[131],"data,":[132],"multi-domain":[134],"100B":[138],"sentences":[139],"achieves":[140],"relative":[141],"WER":[142],"reductions":[143],"up":[145],"12.4%":[147],"without":[148],"21.5%":[152],"from":[156],"400K-hour":[157],"trained":[158],"HAT.":[159]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}
