{"id":"https://openalex.org/W2963774943","doi":"https://doi.org/10.21437/interspeech.2016-1298","title":"Efficient Segmental Cascades for Speech Recognition","display_name":"Efficient Segmental Cascades for Speech Recognition","publication_year":2016,"publication_date":"2016-08-29","ids":{"openalex":"https://openalex.org/W2963774943","doi":"https://doi.org/10.21437/interspeech.2016-1298","mag":"2963774943"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2016-1298","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2016-1298","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2016","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/A5100662187","display_name":"Hao Tang","orcid":"https://orcid.org/0000-0002-2445-2605"},"institutions":[{"id":"https://openalex.org/I160992636","display_name":"Toyota Technological Institute at Chicago","ror":"https://ror.org/02sn5gb64","country_code":"US","type":"education","lineage":["https://openalex.org/I160992636"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hao Tang","raw_affiliation_strings":["Toyota Technological Institute at Chicago"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Technological Institute at Chicago","institution_ids":["https://openalex.org/I160992636"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101432591","display_name":"Weiran Wang","orcid":"https://orcid.org/0009-0000-0843-707X"},"institutions":[{"id":"https://openalex.org/I160992636","display_name":"Toyota Technological Institute at Chicago","ror":"https://ror.org/02sn5gb64","country_code":"US","type":"education","lineage":["https://openalex.org/I160992636"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weiran Wang","raw_affiliation_strings":["Toyota Technological Institute at Chicago"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Technological Institute at Chicago","institution_ids":["https://openalex.org/I160992636"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081022650","display_name":"Kevin Gimpel","orcid":null},"institutions":[{"id":"https://openalex.org/I160992636","display_name":"Toyota Technological Institute at Chicago","ror":"https://ror.org/02sn5gb64","country_code":"US","type":"education","lineage":["https://openalex.org/I160992636"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kevin Gimpel","raw_affiliation_strings":["Toyota Technological Institute at Chicago"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Technological Institute at Chicago","institution_ids":["https://openalex.org/I160992636"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015602781","display_name":"Karen Livescu","orcid":"https://orcid.org/0000-0003-4962-946X"},"institutions":[{"id":"https://openalex.org/I160992636","display_name":"Toyota Technological Institute at Chicago","ror":"https://ror.org/02sn5gb64","country_code":"US","type":"education","lineage":["https://openalex.org/I160992636"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Karen Livescu","raw_affiliation_strings":["Toyota Technological Institute at Chicago"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Technological Institute at Chicago","institution_ids":["https://openalex.org/I160992636"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I160992636"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1903","last_page":"1907"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9994999766349792,"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":0.9987999796867371,"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/discriminative-model","display_name":"Discriminative model","score":0.8016477227210999},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7881503701210022},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.668397843837738},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6482104063034058},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.622901201248169},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.522621214389801},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4884037375450134},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43733227252960205},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4317816495895386},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4259803891181946},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.14330577850341797}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8016477227210999},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7881503701210022},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.668397843837738},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6482104063034058},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.622901201248169},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.522621214389801},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4884037375450134},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43733227252960205},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4317816495895386},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4259803891181946},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.14330577850341797},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/interspeech.2016-1298","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2016-1298","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2016","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"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":17,"referenced_works":["https://openalex.org/W15592790","https://openalex.org/W1591801644","https://openalex.org/W1967325250","https://openalex.org/W2010291496","https://openalex.org/W2097207027","https://openalex.org/W2111732304","https://openalex.org/W2131033001","https://openalex.org/W2146502635","https://openalex.org/W2158575505","https://openalex.org/W2164714022","https://openalex.org/W2395342389","https://openalex.org/W2396384435","https://openalex.org/W2403708103","https://openalex.org/W2521779032","https://openalex.org/W2952472064","https://openalex.org/W2963688273","https://openalex.org/W2963795358"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W1482209366","https://openalex.org/W2110523656","https://openalex.org/W2521627374","https://openalex.org/W2981954115"],"abstract_inverted_index":{"Discriminative":[0],"segmental":[1,35,50,59,69],"models":[2,36],"offer":[3],"a":[4,49,56,96],"way":[5],"to":[6,24,31,104],"incorporate":[7],"flexible":[8],"feature":[9,76],"functions":[10],"into":[11],"speech":[12],"recognition.However,":[13],"their":[14,20],"appeal":[15],"has":[16],"been":[17],"limited":[18],"by":[19,55],"computational":[21],"requirements,":[22],"due":[23],"the":[25,75,79],"large":[26],"number":[27],"of":[28,34,39,67,98],"possible":[29,103],"segments":[30],"consider.Multi-pass":[32],"cascades":[33,70],"introduce":[37],"features":[38],"increasing":[40],"complexity":[41],"in":[42,46,78],"different":[43],"passes,":[44],"where":[45],"each":[47],"pass":[48],"model":[51],"rescores":[52],"lattices":[53],"produced":[54],"previous":[57],"(simpler)":[58],"model.In":[60],"this":[61],"paper,":[62],"we":[63,92],"explore":[64],"several":[65],"ways":[66],"making":[68],"efficient":[71],"and":[72,84,113],"practical:":[73],"reducing":[74,110],"set":[77],"first":[80],"pass,":[81],"frame":[82],"subsampling,":[83],"various":[85],"pruning":[86],"approaches.In":[87],"experiments":[88],"on":[89],"phonetic":[90],"recognition,":[91],"find":[93],"that":[94],"with":[95],"combination":[97],"such":[99],"techniques,":[100],"it":[101],"is":[102],"maintain":[105],"competitive":[106],"performance":[107],"while":[108],"greatly":[109],"decoding,":[111],"pruning,":[112],"training":[114],"time.":[115]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
