{"id":"https://openalex.org/W2951759332","doi":"https://doi.org/10.1109/ncc.2019.8732212","title":"Detection of Vowels in Speech Signals Degraded by Speech-Like Noise","display_name":"Detection of Vowels in Speech Signals Degraded by Speech-Like Noise","publication_year":2019,"publication_date":"2019-02-01","ids":{"openalex":"https://openalex.org/W2951759332","doi":"https://doi.org/10.1109/ncc.2019.8732212","mag":"2951759332"},"language":"en","primary_location":{"id":"doi:10.1109/ncc.2019.8732212","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ncc.2019.8732212","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 National Conference on Communications (NCC)","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/A5059592615","display_name":"Avinash Kumar","orcid":"https://orcid.org/0000-0001-7953-0247"},"institutions":[{"id":"https://openalex.org/I11793825","display_name":"National Institute of Technology Patna","ror":"https://ror.org/056wyhh33","country_code":"IN","type":"education","lineage":["https://openalex.org/I11793825","https://openalex.org/I4210152752"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Avinash Kumar","raw_affiliation_strings":["Dept. of ECE, NIT Patna, Patna, India"],"affiliations":[{"raw_affiliation_string":"Dept. of ECE, NIT Patna, Patna, India","institution_ids":["https://openalex.org/I11793825"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063682063","display_name":"S. Shahnawazuddin","orcid":"https://orcid.org/0000-0002-3916-9693"},"institutions":[{"id":"https://openalex.org/I11793825","display_name":"National Institute of Technology Patna","ror":"https://ror.org/056wyhh33","country_code":"IN","type":"education","lineage":["https://openalex.org/I11793825","https://openalex.org/I4210152752"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Syed Shahnawazuddin","raw_affiliation_strings":["Dept. of ECE, NIT Patna, Patna, India"],"affiliations":[{"raw_affiliation_string":"Dept. of ECE, NIT Patna, Patna, India","institution_ids":["https://openalex.org/I11793825"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069770009","display_name":"Sarmila Garnaik","orcid":null},"institutions":[{"id":"https://openalex.org/I185065464","display_name":"Veer Surendra Sai University of Technology","ror":"https://ror.org/02yghbg68","country_code":"IN","type":"education","lineage":["https://openalex.org/I185065464"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sarmila Garnaik","raw_affiliation_strings":["Dept. of EEE, VSSUT, Odisha, India"],"affiliations":[{"raw_affiliation_string":"Dept. of EEE, VSSUT, Odisha, India","institution_ids":["https://openalex.org/I185065464"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053878164","display_name":"Ishwar Chandra Yadav","orcid":"https://orcid.org/0000-0001-9456-6897"},"institutions":[{"id":"https://openalex.org/I11793825","display_name":"National Institute of Technology Patna","ror":"https://ror.org/056wyhh33","country_code":"IN","type":"education","lineage":["https://openalex.org/I11793825","https://openalex.org/I4210152752"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ishwar Chandra Yadav","raw_affiliation_strings":["Dept. of ECE, NIT Patna, Patna, India"],"affiliations":[{"raw_affiliation_string":"Dept. of ECE, NIT Patna, Patna, India","institution_ids":["https://openalex.org/I11793825"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038771729","display_name":"Gayadhar Pradhan","orcid":"https://orcid.org/0000-0001-7385-6684"},"institutions":[{"id":"https://openalex.org/I11793825","display_name":"National Institute of Technology Patna","ror":"https://ror.org/056wyhh33","country_code":"IN","type":"education","lineage":["https://openalex.org/I11793825","https://openalex.org/I4210152752"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Gayadhar Pradhan","raw_affiliation_strings":["Dept. of ECE, NIT Patna, Patna, India"],"affiliations":[{"raw_affiliation_string":"Dept. of ECE, NIT Patna, Patna, India","institution_ids":["https://openalex.org/I11793825"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5059592615"],"corresponding_institution_ids":["https://openalex.org/I11793825"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.05161409,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"33","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9988999962806702,"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":0.9832000136375427,"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/speech-recognition","display_name":"Speech recognition","score":0.8001935482025146},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7653459310531616},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.7106478810310364},{"id":"https://openalex.org/keywords/vowel","display_name":"Vowel","score":0.574826717376709},{"id":"https://openalex.org/keywords/speech-enhancement","display_name":"Speech enhancement","score":0.5240264534950256},{"id":"https://openalex.org/keywords/voice-activity-detection","display_name":"Voice activity detection","score":0.5140472650527954},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4969525635242462},{"id":"https://openalex.org/keywords/timit","display_name":"TIMIT","score":0.4642634391784668},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44801658391952515},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43860742449760437},{"id":"https://openalex.org/keywords/speech-processing","display_name":"Speech processing","score":0.426156222820282},{"id":"https://openalex.org/keywords/background-noise","display_name":"Background noise","score":0.4159677028656006},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41419291496276855},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.41078975796699524},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.2870674133300781}],"concepts":[{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.8001935482025146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7653459310531616},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.7106478810310364},{"id":"https://openalex.org/C2779581591","wikidata":"https://www.wikidata.org/wiki/Q36244","display_name":"Vowel","level":2,"score":0.574826717376709},{"id":"https://openalex.org/C2776182073","wikidata":"https://www.wikidata.org/wiki/Q7575395","display_name":"Speech enhancement","level":3,"score":0.5240264534950256},{"id":"https://openalex.org/C204201278","wikidata":"https://www.wikidata.org/wiki/Q1332614","display_name":"Voice activity detection","level":3,"score":0.5140472650527954},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4969525635242462},{"id":"https://openalex.org/C2778724510","wikidata":"https://www.wikidata.org/wiki/Q7670405","display_name":"TIMIT","level":3,"score":0.4642634391784668},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44801658391952515},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43860742449760437},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.426156222820282},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.4159677028656006},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41419291496276855},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.41078975796699524},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2870674133300781},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ncc.2019.8732212","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ncc.2019.8732212","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 National Conference on Communications (NCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1524333225","https://openalex.org/W1974387177","https://openalex.org/W1986501036","https://openalex.org/W2000982976","https://openalex.org/W2001105330","https://openalex.org/W2008884494","https://openalex.org/W2030879331","https://openalex.org/W2034766634","https://openalex.org/W2048175496","https://openalex.org/W2064675550","https://openalex.org/W2143303499","https://openalex.org/W2148154194","https://openalex.org/W2168201882","https://openalex.org/W2514707631","https://openalex.org/W2518161968","https://openalex.org/W2739418057","https://openalex.org/W2746756894","https://openalex.org/W4245838404","https://openalex.org/W6631362777"],"related_works":["https://openalex.org/W2597829360","https://openalex.org/W4375869169","https://openalex.org/W2413040788","https://openalex.org/W2921661700","https://openalex.org/W2138406058","https://openalex.org/W2153897396","https://openalex.org/W2048014685","https://openalex.org/W2294333436","https://openalex.org/W2057635330","https://openalex.org/W2157102033"],"abstract_inverted_index":{"Detecting":[0],"vowels":[1,47],"in":[2,48,78,149],"a":[3,8,29,65,134],"noisy":[4,154],"speech":[5,49,60,136],"signal":[6],"is":[7,14,40,61,83,110],"very":[9],"challenging":[10],"task.":[11],"The":[12,74,145],"problem":[13],"further":[15,189],"aggravated":[16],"when":[17],"the":[18,44,81,87,113,117,128,142,158,162,167,178,181,184,191],"noise":[19],"exhibits":[20],"speech-like":[21,53],"characteristics,":[22],"e.g.,":[23],"babble":[24],"noise.":[25,54],"In":[26],"this":[27],"work,":[28],"novel":[30],"front-end":[31,88],"feature":[32],"extraction":[33],"technique":[34],"exploiting":[35],"variational":[36,68],"mode":[37,69],"decomposition":[38],"(VMD)":[39],"proposed":[41,118,146,168],"to":[42,140,188],"improve":[43,190],"detection":[45,192],"of":[46,59,67,80],"data":[50],"degraded":[51],"by":[52],"Each":[55],"short-time":[56],"analysis":[57],"frame":[58],"first":[62],"decomposed":[63],"into":[64],"set":[66],"functions":[70],"(VMFs)":[71],"using":[72,116,166],"VMD.":[73],"logarithmic":[75],"energy":[76],"present":[77],"each":[79],"VMFs":[82],"then":[84],"used":[85],"as":[86,120,122],"features":[89,119,147,169],"for":[90,133],"detecting":[91],"vowels.":[92],"A":[93],"three-class":[94,129],"classifier":[95],"(vowel,":[96],"non-vowel":[97],"and":[98],"silence)":[99],"with":[100],"acoustic":[101],"modeling":[102],"based":[103],"on":[104,112],"long":[105],"short-term":[106],"memory":[107],"(LSTM)":[108],"architecture":[109],"developed":[111],"TIMIT":[114],"database":[115],"well":[121],"mel-frequency":[123],"cepstral":[124],"coefficients":[125],"(MFCC).":[126],"Using":[127],"classifier,":[130],"frame-level":[131],"time-alignments":[132],"given":[135],"utterance":[137],"are":[138,170,186],"obtained":[139,176],"detect":[141],"vowel":[143,163],"regions.":[144],"result":[148],"significantly":[150],"improved":[151],"performance":[152],"under":[153],"test":[155],"conditions":[156],"than":[157],"MFCC":[159],"features.":[160],"Further,":[161],"regions":[164],"detected":[165],"also":[171],"quite":[172],"different":[173],"from":[174],"those":[175],"through":[177],"MFCC.":[179],"Exploiting":[180],"aforementioned":[182],"differences,":[183],"evidences":[185],"combined":[187],"accuracy.":[193]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
