{"id":"https://openalex.org/W3012172444","doi":"https://doi.org/10.1109/apsipaasc47483.2019.9023075","title":"A Study on Low-resource Language Identification","display_name":"A Study on Low-resource Language Identification","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3012172444","doi":"https://doi.org/10.1109/apsipaasc47483.2019.9023075","mag":"3012172444"},"language":"en","primary_location":{"id":"doi:10.1109/apsipaasc47483.2019.9023075","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc47483.2019.9023075","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","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/A5020451447","display_name":"Zhaodi Qi","orcid":null},"institutions":[{"id":"https://openalex.org/I118574674","display_name":"Jiangsu Normal University","ror":"https://ror.org/051hvcm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I118574674"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhaodi Qi","raw_affiliation_strings":["School of Physics and Electronic Engineering, Jiangsu Normal University, Xuzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Physics and Electronic Engineering, Jiangsu Normal University, Xuzhou, China","institution_ids":["https://openalex.org/I118574674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002618865","display_name":"Yong Ma","orcid":"https://orcid.org/0000-0002-1116-0662"},"institutions":[{"id":"https://openalex.org/I118574674","display_name":"Jiangsu Normal University","ror":"https://ror.org/051hvcm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I118574674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Ma","raw_affiliation_strings":["School of Physics and Electronic Engineering, Jiangsu Normal University, Xuzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Physics and Electronic Engineering, Jiangsu Normal University, Xuzhou, China","institution_ids":["https://openalex.org/I118574674"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100940014","display_name":"Mingliang Gu","orcid":null},"institutions":[{"id":"https://openalex.org/I118574674","display_name":"Jiangsu Normal University","ror":"https://ror.org/051hvcm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I118574674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingliang Gu","raw_affiliation_strings":["School of Physics and Electronic Engineering, Jiangsu Normal University, Xuzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Physics and Electronic Engineering, Jiangsu Normal University, Xuzhou, China","institution_ids":["https://openalex.org/I118574674"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5020451447"],"corresponding_institution_ids":["https://openalex.org/I118574674"],"apc_list":null,"apc_paid":null,"fwci":0.4201,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.72886171,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1897","last_page":"1902"},"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9991000294685364,"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":0.9957000017166138,"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/bottleneck","display_name":"Bottleneck","score":0.8378363251686096},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8083729147911072},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6323794722557068},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6010409593582153},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5845734477043152},{"id":"https://openalex.org/keywords/language-identification","display_name":"Language identification","score":0.5480331778526306},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.547795295715332},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5308844447135925},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5233494639396667},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.48935240507125854},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4772926867008209},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.475273072719574},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3859149217605591},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37963688373565674},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.28667330741882324},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07719454169273376},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07702121138572693},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.0694780945777893}],"concepts":[{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.8378363251686096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8083729147911072},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6323794722557068},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6010409593582153},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5845734477043152},{"id":"https://openalex.org/C129792486","wikidata":"https://www.wikidata.org/wiki/Q1050419","display_name":"Language identification","level":3,"score":0.5480331778526306},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.547795295715332},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5308844447135925},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5233494639396667},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.48935240507125854},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4772926867008209},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.475273072719574},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3859149217605591},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37963688373565674},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.28667330741882324},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07719454169273376},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07702121138572693},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0694780945777893},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipaasc47483.2019.9023075","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc47483.2019.9023075","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1524333225","https://openalex.org/W2078169166","https://openalex.org/W2098859361","https://openalex.org/W2104457544","https://openalex.org/W2150769028","https://openalex.org/W2153181479","https://openalex.org/W2172287020","https://openalex.org/W2187089797","https://openalex.org/W2187428966","https://openalex.org/W2219249508","https://openalex.org/W2340176088","https://openalex.org/W2407080277","https://openalex.org/W2696967604","https://openalex.org/W2807627734","https://openalex.org/W2890964092","https://openalex.org/W2952387069","https://openalex.org/W2963063081","https://openalex.org/W2963307329","https://openalex.org/W2963433413","https://openalex.org/W2963436198","https://openalex.org/W2963711940","https://openalex.org/W2964247977","https://openalex.org/W6631362777","https://openalex.org/W6688816777","https://openalex.org/W6703974891","https://openalex.org/W6713762819","https://openalex.org/W6752688541","https://openalex.org/W6764704314"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2127970246","https://openalex.org/W2885125400","https://openalex.org/W1989889224","https://openalex.org/W4382618745","https://openalex.org/W2124808007","https://openalex.org/W2387081965","https://openalex.org/W1886560176","https://openalex.org/W4387913092"],"abstract_inverted_index":{"Modern":[0],"language":[1],"identification":[2],"(LID)":[3],"systems":[4,101],"require":[5],"a":[6],"large":[7],"amount":[8],"of":[9,25,34,93,102],"data":[10,35,65,70,129],"to":[11,52],"train":[12],"language-discriminative":[13],"models,":[14],"either":[15],"statistical":[16],"(e.g.,":[17,21],"i-vector)":[18],"or":[19],"neural":[20],"x-vector).":[22],"Unfortunately,":[23],"most":[24,43],"languages":[26],"in":[27,39],"the":[28,55,69,77,81,109,116],"world":[29],"have":[30],"very":[31],"limited":[32,40],"accumulation":[33],"resources,":[36],"which":[37,67,89],"result":[38],"performance":[41],"on":[42,58,98,107,126],"languages.":[44,60,104],"In":[45],"this":[46],"study,":[47],"two":[48,117],"approaches":[49],"are":[50,119],"investigated":[51],"deal":[53],"with":[54],"LID":[56],"task":[57],"low-resource":[59],"The":[61],"first":[62],"approach":[63,83,118],"is":[64,84],"augmentation,":[66],"enlarges":[68],"set":[71],"by":[72],"incorporating":[73],"various":[74],"distortions":[75],"into":[76],"original":[78],"data;":[79],"and":[80,111,121,130],"second":[82],"multi-lingual":[85],"bottleneck":[86,94],"feature":[87],"extraction,":[88],"extracts":[90],"multiple":[91,103],"sets":[92],"features":[95],"(BNF)":[96],"based":[97],"speech":[99],"recognition":[100],"Experiments":[105],"conducted":[106],"both":[108,127],"i-vector":[110],"x-vector":[112],"models":[113],"demonstrated":[114],"that":[115],"effective,":[120],"can":[122],"obtain":[123],"promising":[124],"results":[125],"in-domain":[128],"out-of-domain":[131],"data.":[132]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}