{"id":"https://openalex.org/W2789497025","doi":"https://doi.org/10.1515/jisys-2017-0618","title":"Discriminative Training Using Noise Robust Integrated Features and Refined HMM Modeling","display_name":"Discriminative Training Using Noise Robust Integrated Features and Refined HMM Modeling","publication_year":2018,"publication_date":"2018-02-20","ids":{"openalex":"https://openalex.org/W2789497025","doi":"https://doi.org/10.1515/jisys-2017-0618","mag":"2789497025"},"language":"en","primary_location":{"id":"doi:10.1515/jisys-2017-0618","is_oa":true,"landing_page_url":"https://doi.org/10.1515/jisys-2017-0618","pdf_url":null,"source":{"id":"https://openalex.org/S2764846071","display_name":"Journal of Intelligent Systems","issn_l":"0334-1860","issn":["0334-1860","2191-026X"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315148","host_organization_name":"IlmuKomputer.Com","host_organization_lineage":["https://openalex.org/P4310315148"],"host_organization_lineage_names":["IlmuKomputer.Com"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1515/jisys-2017-0618","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012812696","display_name":"Mohit Dua","orcid":"https://orcid.org/0000-0001-7071-8323"},"institutions":[{"id":"https://openalex.org/I105094715","display_name":"National Institute of Technology Kurukshetra","ror":"https://ror.org/04909p852","country_code":"IN","type":"education","lineage":["https://openalex.org/I105094715"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Mohit Dua","raw_affiliation_strings":["Department of Computer Engineering , National Institute of Technology , Kurukshetra , India"],"raw_orcid":"https://orcid.org/0000-0001-7071-8323","affiliations":[{"raw_affiliation_string":"Department of Computer Engineering , National Institute of Technology , Kurukshetra , India","institution_ids":["https://openalex.org/I105094715"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008241621","display_name":"Rajesh Kumar Aggarwal","orcid":null},"institutions":[{"id":"https://openalex.org/I105094715","display_name":"National Institute of Technology Kurukshetra","ror":"https://ror.org/04909p852","country_code":"IN","type":"education","lineage":["https://openalex.org/I105094715"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rajesh Kumar Aggarwal","raw_affiliation_strings":["Department of Computer Engineering , National Institute of Technology , Kurukshetra , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering , National Institute of Technology , Kurukshetra , India","institution_ids":["https://openalex.org/I105094715"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059529431","display_name":"Mantosh Biswas","orcid":"https://orcid.org/0000-0001-9027-4432"},"institutions":[{"id":"https://openalex.org/I105094715","display_name":"National Institute of Technology Kurukshetra","ror":"https://ror.org/04909p852","country_code":"IN","type":"education","lineage":["https://openalex.org/I105094715"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mantosh Biswas","raw_affiliation_strings":["Department of Computer Engineering , National Institute of Technology , Kurukshetra , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering , National Institute of Technology , Kurukshetra , India","institution_ids":["https://openalex.org/I105094715"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5012812696"],"corresponding_institution_ids":["https://openalex.org/I105094715"],"apc_list":{"value":1000,"currency":"EUR","value_usd":1078},"apc_paid":{"value":1000,"currency":"EUR","value_usd":1078},"fwci":2.3692,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.91060502,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"29","issue":"1","first_page":"327","last_page":"344"},"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":0.9998999834060669,"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"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9955999851226807,"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/hidden-markov-model","display_name":"Hidden Markov model","score":0.8905714750289917},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.8627436757087708},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8232223987579346},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7566282749176025},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6955690979957581},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6788581609725952},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6146454215049744},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5161746144294739},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.49694207310676575},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.48996463418006897},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45702311396598816},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.42542314529418945},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.17252865433692932}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.8905714750289917},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.8627436757087708},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8232223987579346},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7566282749176025},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6955690979957581},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6788581609725952},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6146454215049744},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5161746144294739},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.49694207310676575},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.48996463418006897},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45702311396598816},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.42542314529418945},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.17252865433692932},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1515/jisys-2017-0618","is_oa":true,"landing_page_url":"https://doi.org/10.1515/jisys-2017-0618","pdf_url":null,"source":{"id":"https://openalex.org/S2764846071","display_name":"Journal of Intelligent Systems","issn_l":"0334-1860","issn":["0334-1860","2191-026X"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315148","host_organization_name":"IlmuKomputer.Com","host_organization_lineage":["https://openalex.org/P4310315148"],"host_organization_lineage_names":["IlmuKomputer.Com"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent Systems","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:caedd938ccb4435e965b2fc96bd2bc3d","is_oa":true,"landing_page_url":"https://doaj.org/article/caedd938ccb4435e965b2fc96bd2bc3d","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Intelligent Systems, Vol 29, Iss 1, Pp 327-344 (2018)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1515/jisys-2017-0618","is_oa":true,"landing_page_url":"https://doi.org/10.1515/jisys-2017-0618","pdf_url":null,"source":{"id":"https://openalex.org/S2764846071","display_name":"Journal of Intelligent Systems","issn_l":"0334-1860","issn":["0334-1860","2191-026X"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310315148","host_organization_name":"IlmuKomputer.Com","host_organization_lineage":["https://openalex.org/P4310315148"],"host_organization_lineage_names":["IlmuKomputer.Com"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W44638342","https://openalex.org/W147488190","https://openalex.org/W1497256448","https://openalex.org/W1539017161","https://openalex.org/W1544810769","https://openalex.org/W1560013842","https://openalex.org/W1578856370","https://openalex.org/W1595159159","https://openalex.org/W1964815273","https://openalex.org/W1974510156","https://openalex.org/W1978274651","https://openalex.org/W1985370384","https://openalex.org/W1992475611","https://openalex.org/W2001016221","https://openalex.org/W2003123121","https://openalex.org/W2007645738","https://openalex.org/W2011581767","https://openalex.org/W2015631279","https://openalex.org/W2055900849","https://openalex.org/W2061140267","https://openalex.org/W2080400971","https://openalex.org/W2084514013","https://openalex.org/W2090861223","https://openalex.org/W2092958002","https://openalex.org/W2121178298","https://openalex.org/W2128454066","https://openalex.org/W2135346934","https://openalex.org/W2148154194","https://openalex.org/W2158808283","https://openalex.org/W2161329381","https://openalex.org/W2254811285","https://openalex.org/W2529829450","https://openalex.org/W2538636254","https://openalex.org/W2543580944","https://openalex.org/W2558350712","https://openalex.org/W2558681280","https://openalex.org/W2560520975","https://openalex.org/W2595741664","https://openalex.org/W2745025069","https://openalex.org/W2760414490","https://openalex.org/W2766110517","https://openalex.org/W3141839452","https://openalex.org/W3149335959","https://openalex.org/W3149794337","https://openalex.org/W4285719527","https://openalex.org/W6813824968"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W2990982991","https://openalex.org/W2167155152","https://openalex.org/W2606551632","https://openalex.org/W156213964","https://openalex.org/W3044690502"],"abstract_inverted_index":{"Abstract":[0],"The":[1,30,80,123,162],"classical":[2],"approach":[3],"to":[4,114,154],"build":[5],"an":[6],"automatic":[7],"speech":[8],"recognition":[9],"(ASR)":[10],"system":[11,75,91],"uses":[12],"different":[13],"feature":[14,50,65,120,173],"extraction":[15],"methods":[16],"at":[17,26],"the":[18,27,42,53,60,68,84,156,159,184],"front":[19],"end":[20],"and":[21,35,52,72,97,107,117,132,147,175],"various":[22],"parameter":[23,177],"classification":[24],"techniques":[25,40],"back":[28],"end.":[29],"Mel-frequency":[31],"cepstral":[32,111],"coefficients":[33],"(MFCC)":[34],"perceptual":[36],"linear":[37],"prediction":[38],"(PLP)":[39],"are":[41,126],"conventional":[43],"approaches":[44],"used":[45],"for":[46,49,64],"many":[47],"years":[48],"extraction,":[51],"hidden":[54],"Markov":[55],"model":[56,141],"(HMM)":[57],"has":[58],"been":[59],"most":[61],"obvious":[62],"selection":[63],"classification.":[66],"However,":[67],"performance":[69],"of":[70,86,139,158],"MFCC-HMM":[71],"PLP-HMM-based":[73],"ASR":[74,90],"degrades":[76],"in":[77],"real-time":[78],"environments.":[79],"proposed":[81,160],"work":[82],"discusses":[83],"implementation":[85],"discriminatively":[87],"trained":[88],"Hindi":[89],"using":[92,128,142,168],"noise":[93],"robust":[94],"integrated":[95,119,172],"features":[96],"refined":[98,127],"HMM":[99,124],"model.":[100],"It":[101],"sequentially":[102],"combines":[103],"MFCC":[104,108],"with":[105,109,170],"PLP":[106],"gammatone-frequency":[110],"coefficient":[112],"(GFCC)":[113],"obtain":[115],"MF-PLP":[116],"MF-GFCC":[118,171],"vectors,":[121],"respectively.":[122],"parameters":[125],"genetic":[129],"algorithm":[130],"(GA)":[131],"particle":[133],"swarm":[134],"optimization":[135],"(PSO).":[136],"Discriminative":[137],"training":[138,167],"acoustic":[140],"maximum":[143],"mutual":[144],"information":[145],"(MMI)":[146],"minimum":[148],"phone":[149],"error":[150],"(MPE)":[151],"is":[152],"preformed":[153],"enhance":[155],"accuracy":[157],"system.":[161],"results":[163,182],"show":[164],"that":[165],"discriminative":[166],"MPE":[169],"vector":[174],"PSO-HMM":[176],"refinement":[178],"gives":[179],"significantly":[180],"better":[181],"than":[183],"other":[185],"implemented":[186],"techniques.":[187]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2025-12-06T23:10:59.065948","created_date":"2025-10-10T00:00:00"}
