{"id":"https://openalex.org/W2998934032","doi":"https://doi.org/10.1109/iccp48234.2019.8959645","title":"Keyword Spotting using Dynamic Time Warping and Convolutional Recurrent Networks","display_name":"Keyword Spotting using Dynamic Time Warping and Convolutional Recurrent Networks","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2998934032","doi":"https://doi.org/10.1109/iccp48234.2019.8959645","mag":"2998934032"},"language":"en","primary_location":{"id":"doi:10.1109/iccp48234.2019.8959645","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccp48234.2019.8959645","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP)","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/A5018699699","display_name":"Erika-Timea Albert","orcid":null},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":true,"raw_author_name":"Erika-Timea Albert","raw_affiliation_strings":["Technical University of Cluj-Napoca,Computer Science Dept,Cluj-Napoca,Romania","Computer Science Dept, Technical University of Cluj-Napoca, Cluj-Napoca, Romania"],"affiliations":[{"raw_affiliation_string":"Technical University of Cluj-Napoca,Computer Science Dept,Cluj-Napoca,Romania","institution_ids":["https://openalex.org/I158333966"]},{"raw_affiliation_string":"Computer Science Dept, Technical University of Cluj-Napoca, Cluj-Napoca, Romania","institution_ids":["https://openalex.org/I158333966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070444955","display_name":"Camelia Lemnaru","orcid":"https://orcid.org/0000-0002-4901-9808"},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Camelia Lemnaru","raw_affiliation_strings":["Technical University of Cluj-Napoca,Computer Science Dept,Cluj-Napoca,Romania","Computer Science Dept, Technical University of Cluj-Napoca, Cluj-Napoca, Romania"],"affiliations":[{"raw_affiliation_string":"Technical University of Cluj-Napoca,Computer Science Dept,Cluj-Napoca,Romania","institution_ids":["https://openalex.org/I158333966"]},{"raw_affiliation_string":"Computer Science Dept, Technical University of Cluj-Napoca, Cluj-Napoca, Romania","institution_ids":["https://openalex.org/I158333966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080452338","display_name":"Mihaela D\u00een\u0219oreanu","orcid":"https://orcid.org/0000-0002-4947-0594"},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Mihaela Dinsoreanu","raw_affiliation_strings":["Technical University of Cluj-Napoca,Computer Science Dept,Cluj-Napoca,Romania","Computer Science Dept, Technical University of Cluj-Napoca, Cluj-Napoca, Romania"],"affiliations":[{"raw_affiliation_string":"Technical University of Cluj-Napoca,Computer Science Dept,Cluj-Napoca,Romania","institution_ids":["https://openalex.org/I158333966"]},{"raw_affiliation_string":"Computer Science Dept, Technical University of Cluj-Napoca, Cluj-Napoca, Romania","institution_ids":["https://openalex.org/I158333966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049230139","display_name":"Rodica Potolea","orcid":"https://orcid.org/0000-0002-7051-3691"},"institutions":[{"id":"https://openalex.org/I158333966","display_name":"Technical University of Cluj-Napoca","ror":"https://ror.org/03r8nwp71","country_code":"RO","type":"education","lineage":["https://openalex.org/I158333966"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Rodica Potolea","raw_affiliation_strings":["Technical University of Cluj-Napoca,Computer Science Dept,Cluj-Napoca,Romania","Computer Science Dept, Technical University of Cluj-Napoca, Cluj-Napoca, Romania"],"affiliations":[{"raw_affiliation_string":"Technical University of Cluj-Napoca,Computer Science Dept,Cluj-Napoca,Romania","institution_ids":["https://openalex.org/I158333966"]},{"raw_affiliation_string":"Computer Science Dept, Technical University of Cluj-Napoca, Cluj-Napoca, Romania","institution_ids":["https://openalex.org/I158333966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5018699699"],"corresponding_institution_ids":["https://openalex.org/I158333966"],"apc_list":null,"apc_paid":null,"fwci":0.3317,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.58457101,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"53","last_page":"60"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9990000128746033,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9990000128746033,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9969000220298767,"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/dynamic-time-warping","display_name":"Dynamic time warping","score":0.8999615907669067},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.800155758857727},{"id":"https://openalex.org/keywords/keyword-spotting","display_name":"Keyword spotting","score":0.7326430082321167},{"id":"https://openalex.org/keywords/grayscale","display_name":"Grayscale","score":0.720679759979248},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.692769467830658},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6767311096191406},{"id":"https://openalex.org/keywords/spotting","display_name":"Spotting","score":0.6742147207260132},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6248854398727417},{"id":"https://openalex.org/keywords/image-warping","display_name":"Image warping","score":0.47544217109680176},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.43819278478622437},{"id":"https://openalex.org/keywords/timit","display_name":"TIMIT","score":0.4128854274749756},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41242292523384094},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2864246964454651},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.2066991627216339}],"concepts":[{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.8999615907669067},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.800155758857727},{"id":"https://openalex.org/C2781213101","wikidata":"https://www.wikidata.org/wiki/Q6398558","display_name":"Keyword spotting","level":2,"score":0.7326430082321167},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.720679759979248},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.692769467830658},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6767311096191406},{"id":"https://openalex.org/C2779506182","wikidata":"https://www.wikidata.org/wiki/Q7580141","display_name":"Spotting","level":2,"score":0.6742147207260132},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6248854398727417},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.47544217109680176},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.43819278478622437},{"id":"https://openalex.org/C2778724510","wikidata":"https://www.wikidata.org/wiki/Q7670405","display_name":"TIMIT","level":3,"score":0.4128854274749756},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41242292523384094},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2864246964454651},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2066991627216339}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccp48234.2019.8959645","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccp48234.2019.8959645","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5199999809265137,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1600744878","https://openalex.org/W1998766338","https://openalex.org/W2072128103","https://openalex.org/W2112796928","https://openalex.org/W2126203737","https://openalex.org/W2171019095","https://openalex.org/W2216056764","https://openalex.org/W2508429489","https://openalex.org/W2793494860","https://openalex.org/W2888845873","https://openalex.org/W3127686677","https://openalex.org/W4231109964","https://openalex.org/W6749432043"],"related_works":["https://openalex.org/W2918559346","https://openalex.org/W2114097550","https://openalex.org/W4286904253","https://openalex.org/W3119978414","https://openalex.org/W2516975559","https://openalex.org/W2347413598","https://openalex.org/W2545741539","https://openalex.org/W3206647229","https://openalex.org/W1969408022","https://openalex.org/W2000885660"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,16,36,97,107,132],"method":[4],"for":[5,69,80],"keyword":[6],"spotting,":[7],"which":[8,30],"first":[9],"converts":[10],"utterances":[11],"to":[12,35,50,96,102,110],"grayscale":[13],"images":[14,27],"via":[15],"modified":[17],"Dynamic":[18],"Time":[19],"Warping":[20],"(DTW)":[21],"algorithm,":[22],"and":[23,126],"then":[24],"splits":[25],"the":[26,58,62,66,77,82,93,123,129],"into":[28],"frames":[29],"are":[31],"fed":[32],"in":[33,115],"sequence":[34],"Convolutional":[37],"Recurrent":[38],"Deep":[39],"Neural":[40],"Network":[41],"(CRDNN).":[42],"DTW":[43,67,78],"is":[44],"employed":[45],"because":[46],"of":[47,65,76,135],"its":[48],"capability":[49],"accurately":[51],"capture":[52],"similarities":[53],"between":[54],"time":[55],"sequences,":[56],"while":[57],"neural":[59],"network":[60,109],"exploits":[61],"textural":[63],"features":[64],"matrix":[68,95],"classification.":[70],"We":[71,118],"explore":[72],"three":[73,88],"alternative":[74],"formulations":[75],"algorithm":[79],"extracting":[81],"similarity":[83,94],"matrices,":[84],"as":[85,87],"well":[86],"different":[89],"conversion":[90],"methods":[91],"from":[92],"gray-scale":[98],"image.":[99],"As":[100],"opposed":[101],"previous":[103],"works,":[104],"we":[105],"employ":[106],"recurrent":[108],"consider":[111],"sequential":[112],"information":[113],"encoded":[114],"image":[116],"segments.":[117],"perform":[119],"several":[120],"evaluations":[121],"on":[122],"TIMIT":[124],"corpus":[125],"find":[127],"that":[128],"system":[130],"reaches":[131],"detection":[133],"performance":[134],"95%.":[136]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
