{"id":"https://openalex.org/W4388821108","doi":"https://doi.org/10.1109/apsipaasc58517.2023.10317562","title":"Investigating the Effectiveness of Speaker Embeddings for Shout Intensity Prediction","display_name":"Investigating the Effectiveness of Speaker Embeddings for Shout Intensity Prediction","publication_year":2023,"publication_date":"2023-10-31","ids":{"openalex":"https://openalex.org/W4388821108","doi":"https://doi.org/10.1109/apsipaasc58517.2023.10317562"},"language":"en","primary_location":{"id":"doi:10.1109/apsipaasc58517.2023.10317562","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc58517.2023.10317562","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 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/A5051293207","display_name":"Takahiro Fukumori","orcid":"https://orcid.org/0000-0002-4317-9704"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Takahiro Fukumori","raw_affiliation_strings":["Ritsumeikan University,Japan","Ritsumeikan University, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan University,Japan","institution_ids":["https://openalex.org/I135768898"]},{"raw_affiliation_string":"Ritsumeikan University, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100571656","display_name":"Taito Ishida","orcid":null},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Taito Ishida","raw_affiliation_strings":["Ritsumeikan University,Japan","Ritsumeikan University, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan University,Japan","institution_ids":["https://openalex.org/I135768898"]},{"raw_affiliation_string":"Ritsumeikan University, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103048025","display_name":"Yoichi Yamashita","orcid":"https://orcid.org/0000-0001-5379-9686"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoichi Yamashita","raw_affiliation_strings":["Ritsumeikan University,Japan","Ritsumeikan University, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan University,Japan","institution_ids":["https://openalex.org/I135768898"]},{"raw_affiliation_string":"Ritsumeikan University, Japan","institution_ids":["https://openalex.org/I135768898"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5051293207"],"corresponding_institution_ids":["https://openalex.org/I135768898"],"apc_list":null,"apc_paid":null,"fwci":0.3491,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66678225,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1838","last_page":"1842"},"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":0.9997000098228455,"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":0.9983999729156494,"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":0.7649937868118286},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6970044374465942},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6401722431182861},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.5580978989601135},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5242060422897339},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4779527187347412},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.4740324318408966},{"id":"https://openalex.org/keywords/speaker-verification","display_name":"Speaker verification","score":0.4623734951019287},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43427056074142456},{"id":"https://openalex.org/keywords/style","display_name":"Style (visual arts)","score":0.4133002460002899},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.38684308528900146},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2982759475708008}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7649937868118286},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6970044374465942},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6401722431182861},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5580978989601135},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5242060422897339},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4779527187347412},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.4740324318408966},{"id":"https://openalex.org/C2982762665","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker verification","level":3,"score":0.4623734951019287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43427056074142456},{"id":"https://openalex.org/C2776445246","wikidata":"https://www.wikidata.org/wiki/Q1792644","display_name":"Style (visual arts)","level":2,"score":0.4133002460002899},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38684308528900146},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2982759475708008},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipaasc58517.2023.10317562","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc58517.2023.10317562","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/5","score":0.6700000166893005,"display_name":"Gender equality"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1538203218","https://openalex.org/W1555937754","https://openalex.org/W2065869176","https://openalex.org/W2096672501","https://openalex.org/W2135131618","https://openalex.org/W2242832615","https://openalex.org/W2890964092","https://openalex.org/W2989825986","https://openalex.org/W3007267003","https://openalex.org/W3015707499","https://openalex.org/W3090169026","https://openalex.org/W3162890625","https://openalex.org/W3163091219","https://openalex.org/W3197276571","https://openalex.org/W3199760712","https://openalex.org/W4385822494","https://openalex.org/W4403059234"],"related_works":["https://openalex.org/W66821593","https://openalex.org/W1521299571","https://openalex.org/W4235705411","https://openalex.org/W204267554","https://openalex.org/W215245425","https://openalex.org/W2134501921","https://openalex.org/W4252590334","https://openalex.org/W2543777506","https://openalex.org/W4317103504","https://openalex.org/W3096066489"],"abstract_inverted_index":{"The":[0],"automatic":[1],"detection":[2,115],"of":[3,15,35,48,107,139,175],"shouted":[4,36,49,113],"speeches":[5],"has":[6,26],"attracted":[7],"much":[8],"research":[9],"attention":[10,66],"as":[11,97,125],"a":[12,30,86,112,135],"core":[13],"technology":[14],"audio":[16],"surveillance":[17],"systems.":[18],"A":[19],"common":[20],"strategy":[21],"in":[22,149],"the":[23,45,60,105,130,156,160,165,172],"past":[24],"literature":[25],"been":[27],"to":[28,67],"train":[29],"binary":[31,162],"classifier":[32],"using":[33],"labels":[34],"or":[37],"normal":[38],"speeches.":[39],"Although":[40],"it":[41],"is":[42,146],"known":[43],"that":[44,79],"acoustic":[46],"properties":[47],"speech":[50,98,114,142,176],"usually":[51],"differ":[52],"among":[53],"speakers,":[54],"especially":[55],"male":[56],"and":[57,71],"female":[58],"groups,":[59],"conventional":[61],"methods":[62],"did":[63],"not":[64],"pay":[65],"encoding":[68],"such":[69,96,108],"personal":[70],"gender-related":[72],"style":[73],"information.":[74],"There":[75],"are":[76,83],"recent":[77],"findings":[78],"speaker":[80,90,109],"embeddings,":[81],"which":[82],"produced":[84],"by":[85],"model":[87],"trained":[88],"for":[89,111,129],"identification,":[91],"can":[92,123],"improve":[93],"other":[94],"tasks":[95],"emotion":[99],"classification.":[100],"Thus,":[101],"this":[102],"paper":[103],"investigates":[104],"effectiveness":[106],"embeddings":[110,122,167],"problem.":[116],"Specifically,":[117],"we":[118],"verify":[119],"whether":[120],"x-vector":[121,166],"work":[124],"effective":[126],"auxiliary":[127],"features":[128],"target":[131],"problem":[132],"compared":[133],"with":[134],"simple":[136],"gender":[137],"label":[138],"an":[140],"input":[141],"whose":[143],"ground":[144],"truth":[145],"generally":[147],"unavailable":[148],"real":[150],"situations.":[151],"Our":[152],"experiments":[153],"on":[154],"predicting":[155],"shout":[157],"intensity":[158],"beyond":[159],"traditional":[161],"classification":[163],"demonstrated":[164],"achieved":[168],"performance":[169],"improvement":[170],"over":[171],"single":[173],"use":[174],"features.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
