{"id":"https://openalex.org/W3136804610","doi":"https://doi.org/10.1109/bigdata50022.2020.9378193","title":"Recipe for Creating a Highly Accurate Wake Word Engine","display_name":"Recipe for Creating a Highly Accurate Wake Word Engine","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3136804610","doi":"https://doi.org/10.1109/bigdata50022.2020.9378193","mag":"3136804610"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","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/A5079851149","display_name":"Buvaneswari Ramanan","orcid":null},"institutions":[{"id":"https://openalex.org/I72090969","display_name":"Nokia (United States)","ror":"https://ror.org/038km2573","country_code":"US","type":"company","lineage":["https://openalex.org/I2738502077","https://openalex.org/I72090969"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Buvaneswari Ramanan","raw_affiliation_strings":["Nokia Bell Labs, Murray Hill, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Nokia Bell Labs, Murray Hill, NJ, USA","institution_ids":["https://openalex.org/I72090969"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040352022","display_name":"Lawrence Drabeck","orcid":null},"institutions":[{"id":"https://openalex.org/I72090969","display_name":"Nokia (United States)","ror":"https://ror.org/038km2573","country_code":"US","type":"company","lineage":["https://openalex.org/I2738502077","https://openalex.org/I72090969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lawrence Drabeck","raw_affiliation_strings":["Nokia Bell Labs, Murray Hill, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Nokia Bell Labs, Murray Hill, NJ, USA","institution_ids":["https://openalex.org/I72090969"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069403056","display_name":"Thomas Woo","orcid":"https://orcid.org/0009-0003-2028-5533"},"institutions":[{"id":"https://openalex.org/I72090969","display_name":"Nokia (United States)","ror":"https://ror.org/038km2573","country_code":"US","type":"company","lineage":["https://openalex.org/I2738502077","https://openalex.org/I72090969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thomas Woo","raw_affiliation_strings":["Nokia Bell Labs, Murray Hill, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Nokia Bell Labs, Murray Hill, NJ, USA","institution_ids":["https://openalex.org/I72090969"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060799266","display_name":"Troy Cauble","orcid":null},"institutions":[{"id":"https://openalex.org/I72090969","display_name":"Nokia (United States)","ror":"https://ror.org/038km2573","country_code":"US","type":"company","lineage":["https://openalex.org/I2738502077","https://openalex.org/I72090969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Troy Cauble","raw_affiliation_strings":["Nokia Bell Labs, Murray Hill, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Nokia Bell Labs, Murray Hill, NJ, USA","institution_ids":["https://openalex.org/I72090969"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005614987","display_name":"Anil Rana","orcid":"https://orcid.org/0000-0001-7476-6234"},"institutions":[{"id":"https://openalex.org/I72090969","display_name":"Nokia (United States)","ror":"https://ror.org/038km2573","country_code":"US","type":"company","lineage":["https://openalex.org/I2738502077","https://openalex.org/I72090969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anil Rana","raw_affiliation_strings":["Nokia Bell Labs, Murray Hill, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Nokia Bell Labs, Murray Hill, NJ, USA","institution_ids":["https://openalex.org/I72090969"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5079851149"],"corresponding_institution_ids":["https://openalex.org/I72090969"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19368005,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4734","last_page":"4740"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":1.0,"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/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/T11309","display_name":"Music and Audio Processing","score":0.9998999834060669,"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/wake","display_name":"Wake","score":0.858173131942749},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.7002972364425659},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6213455200195312},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5849465131759644},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33659040927886963},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1967422366142273},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1320236325263977}],"concepts":[{"id":"https://openalex.org/C48939323","wikidata":"https://www.wikidata.org/wiki/Q294879","display_name":"Wake","level":2,"score":0.858173131942749},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.7002972364425659},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6213455200195312},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5849465131759644},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33659040927886963},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1967422366142273},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1320236325263977},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1496120315","https://openalex.org/W2034940213","https://openalex.org/W2406222150","https://openalex.org/W2407023693","https://openalex.org/W2407080277","https://openalex.org/W2587529061","https://openalex.org/W2591789009","https://openalex.org/W2602634800","https://openalex.org/W2617258110","https://openalex.org/W2696967604","https://openalex.org/W2774918761","https://openalex.org/W2887528618","https://openalex.org/W2888797456","https://openalex.org/W2891138528","https://openalex.org/W2936774411","https://openalex.org/W2953219395","https://openalex.org/W2963414149","https://openalex.org/W2972541648","https://openalex.org/W2972572074","https://openalex.org/W2975761646","https://openalex.org/W3007380371","https://openalex.org/W3008587939","https://openalex.org/W3015995734","https://openalex.org/W3098773154","https://openalex.org/W6713762819","https://openalex.org/W6714171909","https://openalex.org/W6753500843","https://openalex.org/W6768126892","https://openalex.org/W6768297763","https://openalex.org/W6771250757"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2757285599","https://openalex.org/W2376025146","https://openalex.org/W161151693","https://openalex.org/W2955635855","https://openalex.org/W3195741387","https://openalex.org/W4381570180","https://openalex.org/W4324257240","https://openalex.org/W2314669870"],"abstract_inverted_index":{"Achieving":[0],"good":[1],"accuracy":[2],"in":[3,22,66],"Deep":[4],"Learning":[5],"(DL)":[6],"models":[7],"for":[8,91,196,242],"Wake":[9],"Word":[10],"Engines":[11],"(WWE)":[12],"strongly":[13],"depends":[14],"on":[15],"using":[16,117,131],"the":[17,29,34,42,48,53,68,76,99,108,111,114,122,127,140,189,218,224],"right":[18],"type":[19],"of":[20,31,36,44,78,101,110,116,142,166,181,211,232],"dataset":[21,26],"training.":[23],"Important":[24],"WWE":[25,69,92,190],"dimensions":[27],"are":[28,85,135,240],"number":[30],"wake":[32,37,128,175,243],"words,":[33,41],"ratio":[35,106,180],"words":[38,46,119,129,133],"to":[39,52,103,177],"non-wake":[40,45,104,118,132,163,178],"composition":[43,109,165],"and":[47,63,74,93,107,155,160,183,215,236],"augmentation":[49,82,148],"techniques":[50,83],"applied":[51],"training":[54],"dataset.":[55],"In":[56],"this":[57],"paper,":[58],"we":[59],"make":[60],"two":[61],"crucial":[62],"first-of-a-kind":[64],"contributions":[65],"improving":[67],"performance:":[70],"(a)":[71,146],"Extensively":[72],"studying":[73],"proving":[75],"effectiveness":[77,141],"three":[79],"potent":[80],"audio":[81,171],"that":[84,134,188],"previously":[86],"not":[87],"studied":[88],"or":[89],"less-studied":[90],"(b)":[94,161],"Providing":[95],"clear":[96],"insight":[97],"into":[98],"effect":[100],"wake-word":[102],"word":[105,164,176,179,244],"latter,":[112],"specifically":[113],"impact":[115],"uttered":[120],"by":[121],"speakers":[123],"who":[124],"also":[125],"utter":[126],"(vs)":[130],"mined":[136],"from":[137],"elsewhere,We":[138],"prove":[139],"our":[143,194],"recipe":[144,195],"of:":[145],"Data":[147],"with":[149,158,193],"Ambient":[150],"Sound":[151],"addition,":[152],"Reverberation,":[153],"Echo":[154],"Speed":[156],"along":[157],"SpecAugment":[159],"A":[162],"Common":[167],"Voice":[168],"plus":[169],"Freesound":[170],"slices":[172],"at":[173,204],"a":[174,199],"1:15":[182],"1:20":[184],"respectively.":[185],"We":[186],"show":[187],"model":[191,228],"trained":[192,227],"`Computer'":[197],"exhibits":[198,229],"False":[200,206],"Reject":[201],"Rate":[202],"(FRR)":[203],"1":[205],"Alarm":[207],"(FA)":[208],"per":[209],"hour":[210],"0%,":[212],"5.6%,":[213],"0.4%,":[214],"0.7%":[216],"under":[217],"four":[219],"test":[220],"environments":[221],"trialed,":[222],"whereas,":[223],"standard":[225],"technique":[226],"an":[230],"FRR":[231],"1.1%,":[233],"18.3%,":[234],"3.7%,":[235],"3.7%.":[237],"Similar":[238],"results":[239],"observed":[241],"`Crystal'.":[245]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
