乐胖代购免代理版

{"id":"https://openalex.org/W2938873567","doi":"https://doi.org/10.1109/icassp.2019.8683479","title":"Adversarial Examples for Improving End-to-end Attention-based Small-footprint Keyword Spotting","display_name":"Adversarial Examples for Improving End-to-end Attention-based Small-footprint Keyword Spotting","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2938873567","doi":"https://doi.org/10.1109/icassp.2019.8683479","mag":"2938873567"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8683479","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_indexed_in_scopus":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":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5100359238","display_name":"Xiong Wang","orcid":"https://orcid.org/0000-0002-1343-0339"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"funder","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiong Wang","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102880200","display_name":"Sining Sun","orcid":"https://orcid.org/0000-0002-2642-5096"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"funder","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sining Sun","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042775692","display_name":"Changhao Shan","orcid":null},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"funder","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changhao Shan","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018722799","display_name":"Jingyong Hou","orcid":"https://orcid.org/0000-0002-1861-7776"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"funder","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyong Hou","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100668966","display_name":"Lei Xie","orcid":"https://orcid.org/0000-0001-8234-0823"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"funder","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Xie","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042802004","display_name":"Li Shen","orcid":"https://orcid.org/0000-0001-5659-3464"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shen Li","raw_affiliation_strings":["Mobvoi AI Lab, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Mobvoi AI Lab, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101535850","display_name":"Xin Lei","orcid":"https://orcid.org/0000-0002-2466-3891"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Lei","raw_affiliation_strings":["Mobvoi AI Lab, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Mobvoi AI Lab, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.848,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":38,"citation_normalized_percentile":{"value":0.887464,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9979,"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":0.9979,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9967,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9912,"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/keyword-spotting","display_name":"Keyword spotting","score":0.827597},{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.64501727},{"id":"https://openalex.org/keywords/footprint","display_name":"Footprint","score":0.62002504},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.47998378}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8363819},{"id":"https://openalex.org/C2781213101","wikidata":"https://www.wikidata.org/wiki/Q6398558","display_name":"Keyword spotting","level":2,"score":0.827597},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.82145476},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.64501727},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.62002504},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.59846735},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5257583},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.47998378},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.45584363},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.43473828},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42690504},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42036557},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.33007926},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07156569},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8683479","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_indexed_in_scopus":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":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":26,"referenced_works":["https://openalex.org/W1496120315","https://openalex.org/W1522301498","https://openalex.org/W1553469512","https://openalex.org/W1673923490","https://openalex.org/W1945616565","https://openalex.org/W1979256737","https://openalex.org/W2015688877","https://openalex.org/W2034940213","https://openalex.org/W2056986588","https://openalex.org/W2112028260","https://openalex.org/W2114097550","https://openalex.org/W2145215282","https://openalex.org/W2157331557","https://openalex.org/W2510867321","https://openalex.org/W2584329820","https://openalex.org/W2613303405","https://openalex.org/W2811405065","https://openalex.org/W2886949521","https://openalex.org/W2962684181","https://openalex.org/W2963207607","https://openalex.org/W2963389226","https://openalex.org/W2963414149","https://openalex.org/W2963917611","https://openalex.org/W2964121744","https://openalex.org/W2964153729","https://openalex.org/W2964301649"],"related_works":["https://openalex.org/W4385822657","https://openalex.org/W3094700205","https://openalex.org/W2938873567","https://openalex.org/W2905687271","https://openalex.org/W2507580616","https://openalex.org/W2165968459","https://openalex.org/W2132174924","https://openalex.org/W2040150569","https://openalex.org/W1989866424","https://openalex.org/W1983393909"],"abstract_inverted_index":{"In":[0,56,80],"this":[1],"paper,":[2],"we":[3,89,124],"explore":[4],"the":[5,52,57,67,95,109,121],"use":[6],"of":[7,77],"adversarial":[8,78,92,114],"examples":[9,93,105,115],"for":[10],"improving":[11],"a":[12,39,43,62,84,144,148],"neural":[13,29],"network":[14,30],"based":[15],"keyword":[16],"spotting":[17],"(KWS)":[18],"system.":[19],"Specially,":[20],"in":[21],"our":[22,81],"system,":[23],"an":[24],"effective":[25],"and":[26,101,129],"small-footprint":[27],"attention-based":[28,86],"model":[31],"is":[32,36,47,61],"used.":[33],"Adversarial":[34],"example":[35,41],"defined":[37],"as":[38,74,116],"misclassified":[40],"by":[42],"model,":[44,88,123],"but":[45],"it":[46,60],"only":[48],"slightly":[49],"skewed":[50],"from":[51,147],"original":[53],"correctly-classified":[54],"one.":[55],"KWS":[58,87,110,122],"task,":[59],"natural":[63],"idea":[64],"to":[65,119],"regard":[66],"false":[68,71,130,137],"alarmed":[69],"or":[70],"rejected":[72],"queries":[73],"some":[75],"kind":[76],"examples.":[79],"work,":[82],"given":[83],"well-trained":[85],"first":[90],"generate":[91],"using":[94],"fast":[96],"gradient":[97],"sign":[98],"method":[99],"(FGSM)":[100],"find":[102],"that":[103],"these":[104,113],"can":[106],"dramatically":[107],"degrade":[108],"performance.":[111],"Using":[112],"augmented":[117],"data":[118],"retrain":[120],"finally":[125],"achieve":[126],"45.6%":[127],"relative":[128],"reject":[131],"rate":[132,139],"(FRR)":[133],"reduction":[134],"at":[135],"1.0":[136],"alarm":[138],"(FAR)":[140],"per":[141],"hour":[142],"on":[143],"collected":[145],"dataset":[146],"smart":[149],"speaker.":[150]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2938873567","counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":5}],"updated_date":"2025-04-16T03:03:57.497131","created_date":"2019-04-25"}