{"id":"https://openalex.org/W2022182953","doi":"https://doi.org/10.1109/ncc.2014.6811382","title":"A Bayesian approach to speaker normalization using vowel formant frequency","display_name":"A Bayesian approach to speaker normalization using vowel formant frequency","publication_year":2014,"publication_date":"2014-02-01","ids":{"openalex":"https://openalex.org/W2022182953","doi":"https://doi.org/10.1109/ncc.2014.6811382","mag":"2022182953"},"language":"en","primary_location":{"id":"doi:10.1109/ncc.2014.6811382","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ncc.2014.6811382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 Twentieth 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/A5103133511","display_name":"Dhananjay Ram","orcid":"https://orcid.org/0000-0003-1822-9199"},"institutions":[{"id":"https://openalex.org/I94234084","display_name":"Indian Institute of Technology Kanpur","ror":"https://ror.org/05pjsgx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I94234084"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Dhananjay Ram","raw_affiliation_strings":["Indian Institute of Technology, Kanpur","Indian Institute of Technology Kanpur \u2028"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology, Kanpur","institution_ids":["https://openalex.org/I94234084"]},{"raw_affiliation_string":"Indian Institute of Technology Kanpur \u2028","institution_ids":["https://openalex.org/I94234084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049715298","display_name":"Debasis Kundu","orcid":"https://orcid.org/0000-0002-9141-422X"},"institutions":[{"id":"https://openalex.org/I94234084","display_name":"Indian Institute of Technology Kanpur","ror":"https://ror.org/05pjsgx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I94234084"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Debasis Kundu","raw_affiliation_strings":["Indian Institute of Technology, Kanpur","Indian Institute of Technology Kanpur \u2028"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology, Kanpur","institution_ids":["https://openalex.org/I94234084"]},{"raw_affiliation_string":"Indian Institute of Technology Kanpur \u2028","institution_ids":["https://openalex.org/I94234084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085503354","display_name":"Rajesh M. Hegde","orcid":"https://orcid.org/0000-0002-6142-7724"},"institutions":[{"id":"https://openalex.org/I94234084","display_name":"Indian Institute of Technology Kanpur","ror":"https://ror.org/05pjsgx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I94234084"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rajesh M. Hegde","raw_affiliation_strings":["Indian Institute of Technology, Kanpur","Indian Institute of Technology Kanpur \u2028"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology, Kanpur","institution_ids":["https://openalex.org/I94234084"]},{"raw_affiliation_string":"Indian Institute of Technology Kanpur \u2028","institution_ids":["https://openalex.org/I94234084"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103133511"],"corresponding_institution_ids":["https://openalex.org/I94234084"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07550162,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"124","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9994999766349792,"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/T11309","display_name":"Music and Audio Processing","score":0.9984999895095825,"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/vocal-tract","display_name":"Vocal tract","score":0.8060475587844849},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.7521378993988037},{"id":"https://openalex.org/keywords/formant","display_name":"Formant","score":0.7007948160171509},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.6139469742774963},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6110338568687439},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.6070900559425354},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5941569209098816},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5776200890541077},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.5757438540458679},{"id":"https://openalex.org/keywords/vowel","display_name":"Vowel","score":0.5118348002433777},{"id":"https://openalex.org/keywords/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.5060951113700867},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4477955996990204},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4270840287208557}],"concepts":[{"id":"https://openalex.org/C47401133","wikidata":"https://www.wikidata.org/wiki/Q748953","display_name":"Vocal tract","level":2,"score":0.8060475587844849},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.7521378993988037},{"id":"https://openalex.org/C158215666","wikidata":"https://www.wikidata.org/wiki/Q1414685","display_name":"Formant","level":3,"score":0.7007948160171509},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.6139469742774963},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6110338568687439},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6070900559425354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5941569209098816},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5776200890541077},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.5757438540458679},{"id":"https://openalex.org/C2779581591","wikidata":"https://www.wikidata.org/wiki/Q36244","display_name":"Vowel","level":2,"score":0.5118348002433777},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.5060951113700867},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4477955996990204},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4270840287208557},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ncc.2014.6811382","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ncc.2014.6811382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 Twentieth National Conference on Communications (NCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5","score":0.5}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2004866931","https://openalex.org/W2027995896","https://openalex.org/W2070696251","https://openalex.org/W2087441176","https://openalex.org/W2090365329","https://openalex.org/W2144242878","https://openalex.org/W2156886787","https://openalex.org/W2164931619","https://openalex.org/W4231949113"],"related_works":["https://openalex.org/W2046073792","https://openalex.org/W4254341835","https://openalex.org/W1748856376","https://openalex.org/W2086580720","https://openalex.org/W1909584822","https://openalex.org/W2001425423","https://openalex.org/W2061217898","https://openalex.org/W2894697037","https://openalex.org/W2045900265","https://openalex.org/W2050311283"],"abstract_inverted_index":{"Large":[0],"variation":[1,46],"in":[2,23,47,60],"speakers":[3],"causes":[4],"significant":[5],"performance":[6,120],"degradation":[7,22],"of":[8,49,90],"a":[9,28,79,87,97,119],"speaker":[10,34],"independent":[11,114],"speech":[12],"recognition":[13],"system.":[14],"In":[15],"an":[16],"attempt":[17],"to":[18,32],"compensate":[19],"for":[20,109,122],"this":[21,25],"performance,":[24],"paper":[26],"proposes":[27],"novel":[29],"Bayesian":[30,80,124],"approach":[31,81],"estimate":[33],"normalization":[35,66],"parameters.":[36],"An":[37],"affine":[38],"model":[39,58],"is":[40,103],"used":[41,59],"here,":[42],"which":[43,82],"captures":[44],"the":[45,50,56,84,123],"length":[48,65],"vocal":[51,63],"tract":[52,64],"more":[53],"effectively":[54],"than":[55],"linear":[57],"literature.":[61],"The":[62],"(VTLN)":[67],"parameters":[68],"are":[69,107],"estimated":[70],"using":[71],"Least":[72],"Squares":[73],"Estimation":[74],"(LSE)":[75],"as":[76,78],"well":[77],"utilizes":[83],"Gibbs":[85],"sampler,":[86],"special":[88],"type":[89],"Markov":[91],"Chain":[92],"Monte":[93],"Carlo":[94],"method.":[95],"Finally,":[96],"Mahalanobis":[98],"distance":[99],"based":[100],"vowel":[101],"recognizer":[102],"proposed":[104],"and":[105,113],"experiments":[106],"performed":[108],"both":[110],"gender":[111],"dependent":[112],"cases.":[115],"Results":[116],"clearly":[117],"indicate":[118],"improvement":[121],"case":[125],"over":[126],"LSE.":[127]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
