{"id":"https://openalex.org/W2808068608","doi":"https://doi.org/10.1145/3177457.3191708","title":"Speaker Independent Isolated Word Recognition based on ANOVA and IFS","display_name":"Speaker Independent Isolated Word Recognition based on ANOVA and IFS","publication_year":2018,"publication_date":"2018-01-08","ids":{"openalex":"https://openalex.org/W2808068608","doi":"https://doi.org/10.1145/3177457.3191708","mag":"2808068608"},"language":"en","primary_location":{"id":"doi:10.1145/3177457.3191708","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3177457.3191708","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Conference on Computer Modeling and Simulation","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/A5084304087","display_name":"Saswati Debnath","orcid":"https://orcid.org/0000-0003-4246-3970"},"institutions":[{"id":"https://openalex.org/I151903974","display_name":"National Institute Of Technology Silchar","ror":"https://ror.org/001ws2a36","country_code":"IN","type":"education","lineage":["https://openalex.org/I151903974"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Saswati Debnath","raw_affiliation_strings":["Computer Science & Engineering Dept., NIT Silchar, Assam, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science & Engineering Dept., NIT Silchar, Assam, India","institution_ids":["https://openalex.org/I151903974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010493101","display_name":"Pinki Roy","orcid":"https://orcid.org/0000-0003-0896-1757"},"institutions":[{"id":"https://openalex.org/I151903974","display_name":"National Institute Of Technology Silchar","ror":"https://ror.org/001ws2a36","country_code":"IN","type":"education","lineage":["https://openalex.org/I151903974"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pinki Roy","raw_affiliation_strings":["Computer Science & Engineering Dept., NIT Silchar, Assam, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science & Engineering Dept., NIT Silchar, Assam, India","institution_ids":["https://openalex.org/I151903974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I151903974"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06191403,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"10","issue":null,"first_page":"92","last_page":"97"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9976000189781189,"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":0.9976000189781189,"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/T13717","display_name":"Advanced Algorithms and Applications","score":0.9778000116348267,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9735999703407288,"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/computer-science","display_name":"Computer science","score":0.7201482653617859},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.7008212208747864},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6926202178001404},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6912598609924316},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6830861568450928},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6225877404212952},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6038316488265991},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5577751994132996},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.5256506204605103},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5248513221740723},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.5163235068321228},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.43777328729629517},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42278504371643066},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4202668368816376},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4126034379005432}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7201482653617859},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.7008212208747864},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6926202178001404},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6912598609924316},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6830861568450928},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6225877404212952},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6038316488265991},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5577751994132996},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5256506204605103},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5248513221740723},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.5163235068321228},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.43777328729629517},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42278504371643066},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4202668368816376},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4126034379005432},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3177457.3191708","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3177457.3191708","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Conference on Computer Modeling and Simulation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W638190039","https://openalex.org/W1986964674","https://openalex.org/W1989298127","https://openalex.org/W2007765030","https://openalex.org/W2019012479","https://openalex.org/W2026537887","https://openalex.org/W2047444359","https://openalex.org/W2122410182","https://openalex.org/W2131871423","https://openalex.org/W2148154194","https://openalex.org/W2148603752","https://openalex.org/W2156017586","https://openalex.org/W2159549127","https://openalex.org/W2167101736","https://openalex.org/W2171404280","https://openalex.org/W2423892468","https://openalex.org/W2479163084","https://openalex.org/W2525133911","https://openalex.org/W2623293810","https://openalex.org/W2967922574","https://openalex.org/W3013682396","https://openalex.org/W3142482781","https://openalex.org/W4253380682","https://openalex.org/W6633431331","https://openalex.org/W7067929970"],"related_works":["https://openalex.org/W2349769824","https://openalex.org/W2914532148","https://openalex.org/W2372625757","https://openalex.org/W4313178214","https://openalex.org/W2970653563","https://openalex.org/W3093766508","https://openalex.org/W2012393389","https://openalex.org/W2899717399","https://openalex.org/W1494724239","https://openalex.org/W6057950"],"abstract_inverted_index":{"A":[0],"crucial":[1],"step":[2],"of":[3,52,59,64,74,84,94,114,165,207],"speaker":[4,42],"independent":[5,43],"isolated":[6,44],"word":[7,45,131],"recognition":[8,46,132,222],"is":[9,31,127,141,211],"to":[10,35,80,103,108,128,200,212],"extract":[11,81],"meaningful":[12,20],"information":[13],"from":[14],"speech":[15,85],"signal.":[16],"Speech":[17],"signal":[18,86],"contain":[19],"acoustic":[21,82],"features":[22,29,83,106,136,216],"and":[23,27,66,87,97,107,119,159,172,224],"selecting":[24,49,201],"the":[25,124,130,178,196,208,221,226],"significant":[26,53,135,215],"optimal":[28,50],"set":[30,217],"an":[32],"important":[33],"aspect":[34],"improve":[36,129,220],"accuracy.":[37],"This":[38],"paper":[39],"proposes":[40],"a":[41],"model":[47,57],"by":[48],"number":[51],"features.":[54],"The":[55,112,138,205],"proposed":[56,209],"consists":[58],"feature":[60,65,67,99,120,126,188],"extraction,":[61],"statistical":[62,91,116],"analysis":[63,117,140,180],"selection":[68,100,121,189],"technique.":[69],"We":[70],"use":[71],"13th":[72],"dimension":[73],"Mel":[75],"frequency":[76],"cepstral":[77,125],"coefficient":[78],"(MFCC)":[79],"after":[88],"that":[89,185,218],"apply":[90],"analysis,":[92],"Analysis":[93],"Variance":[95],"(ANOVA)":[96],"incremental":[98],"(IFS)":[101],"technique":[102,122,190],"investigate":[104],"efficient":[105],"rank":[109],"them":[110],"accordingly.":[111],"objective":[113],"applying":[115],"algorithm":[118],"on":[123],"performance":[133],"using":[134,144,233],"set.":[137],"experimental":[139,179],"carried":[142],"out":[143],"some":[145],"machine":[146,157],"learning":[147],"techniques":[148],"such":[149],"as":[150,198],"Artificial":[151],"Neural":[152],"Network":[153],"(ANN),":[154],"Support":[155],"vector":[156],"(SVM)":[158],"Naive":[160],"Bayes":[161],"(NB)":[162],"classifier.":[163],"Performance":[164],"each":[166],"individual":[167],"classifier":[168,197],"has":[169,182,230],"been":[170,183,231],"evaluated":[171],"described":[173],"in":[174],"this":[175],"paper.":[176],"From":[177],"it":[181],"observed":[184],"ANOVA":[186],"with":[187],"provide":[191],"better":[192],"result":[193],"for":[194],"all":[195,202],"compared":[199],"MFCC":[203],"feature.":[204],"aim":[206],"method":[210],"select":[213],"minimum":[214],"can":[219],"rate":[223],"reduce":[225],"dimension.":[227],"Recognition":[228],"accuracy":[229],"achieved":[232],"our":[234],"own":[235],"recorded":[236],"English":[237],"digit":[238],"database.":[239]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}