{"id":"https://openalex.org/W2073762424","doi":"https://doi.org/10.1145/1929908.1929914","title":"Interruption Point Detection of Spontaneous Speech Using Inter-Syllable Boundary-Based Prosodic Features","display_name":"Interruption Point Detection of Spontaneous Speech Using Inter-Syllable Boundary-Based Prosodic Features","publication_year":2011,"publication_date":"2011-03-01","ids":{"openalex":"https://openalex.org/W2073762424","doi":"https://doi.org/10.1145/1929908.1929914","mag":"2073762424"},"language":"en","primary_location":{"id":"doi:10.1145/1929908.1929914","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1929908.1929914","pdf_url":null,"source":{"id":"https://openalex.org/S56575750","display_name":"ACM Transactions on Asian Language Information Processing","issn_l":"1530-0226","issn":["1530-0226","1558-3430"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Asian Language Information Processing","raw_type":"journal-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/A5103251327","display_name":"Chung\u2010Hsien Wu","orcid":"https://orcid.org/0000-0002-3947-2123"},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chung-Hsien Wu","raw_affiliation_strings":["National Cheng Kung University","National Cheng-Kung University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Cheng Kung University","institution_ids":["https://openalex.org/I91807558"]},{"raw_affiliation_string":"National Cheng-Kung University","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101145803","display_name":"Wei-Bin Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Wei-Bin Liang","raw_affiliation_strings":["National Cheng Kung University","National Cheng-Kung University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Cheng Kung University","institution_ids":["https://openalex.org/I91807558"]},{"raw_affiliation_string":"National Cheng-Kung University","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049377418","display_name":"Jui\u2010Feng Yeh","orcid":"https://orcid.org/0000-0003-2798-1569"},"institutions":[{"id":"https://openalex.org/I183570559","display_name":"National Chiayi University","ror":"https://ror.org/04gknbs13","country_code":"TW","type":"education","lineage":["https://openalex.org/I183570559"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jui-Feng Yeh","raw_affiliation_strings":["National Chiayi University","National Chiayi University#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Chiayi University","institution_ids":["https://openalex.org/I183570559"]},{"raw_affiliation_string":"National Chiayi University#TAB#","institution_ids":["https://openalex.org/I183570559"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4396,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.73426928,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"10","issue":"1","first_page":"1","last_page":"21"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9997000098228455,"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":0.9997000098228455,"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/T10403","display_name":"Phonetics and Phonology Research","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12031","display_name":"Speech and dialogue systems","score":0.9923999905586243,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.7642413377761841},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.7079543471336365},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6806463003158569},{"id":"https://openalex.org/keywords/syllable","display_name":"Syllable","score":0.5891643166542053},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.46430662274360657},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.4272475838661194},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.41655072569847107},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35117200016975403},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3499144911766052}],"concepts":[{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7642413377761841},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.7079543471336365},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6806463003158569},{"id":"https://openalex.org/C109089402","wikidata":"https://www.wikidata.org/wiki/Q8188","display_name":"Syllable","level":2,"score":0.5891643166542053},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.46430662274360657},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.4272475838661194},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.41655072569847107},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35117200016975403},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3499144911766052}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1929908.1929914","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1929908.1929914","pdf_url":null,"source":{"id":"https://openalex.org/S56575750","display_name":"ACM Transactions on Asian Language Information Processing","issn_l":"1530-0226","issn":["1530-0226","1558-3430"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Asian Language Information Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6000000238418579,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G2513745598","display_name":null,"funder_award_id":"NSC 98-2221-E-006-139-MY3","funder_id":"https://openalex.org/F4320321040","funder_display_name":"National Science Council"}],"funders":[{"id":"https://openalex.org/F4320321040","display_name":"National Science Council","ror":"https://ror.org/02kv4zf79"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W11234093","https://openalex.org/W118623719","https://openalex.org/W1501838223","https://openalex.org/W1592796124","https://openalex.org/W2051434435","https://openalex.org/W2065388812","https://openalex.org/W2088921122","https://openalex.org/W2119340951","https://openalex.org/W2134353544","https://openalex.org/W2146101954","https://openalex.org/W2146848812","https://openalex.org/W2147880316","https://openalex.org/W2156515921","https://openalex.org/W2161274063","https://openalex.org/W2165487751","https://openalex.org/W2165836175","https://openalex.org/W2169058332","https://openalex.org/W2405114413","https://openalex.org/W2426479676","https://openalex.org/W2556975121","https://openalex.org/W2949468472","https://openalex.org/W2952387069","https://openalex.org/W2997779899","https://openalex.org/W3216401400","https://openalex.org/W4253482865"],"related_works":["https://openalex.org/W2356597680","https://openalex.org/W2150890698","https://openalex.org/W2061937230","https://openalex.org/W2401394187","https://openalex.org/W4245698648","https://openalex.org/W2132658536","https://openalex.org/W3133710586","https://openalex.org/W2125964738","https://openalex.org/W2098529290","https://openalex.org/W2026402306"],"abstract_inverted_index":{"This":[0],"article":[1],"presents":[2],"a":[3,31,70],"probabilistic":[4],"scheme":[5],"for":[6,171],"detecting":[7],"the":[8,23,47,52,56,61,74,78,92,97,112,117,120,123,127,131,140,145,160,177,190,195,201,206,228,240],"interruption":[9,241],"point":[10,242],"(IP)":[11],"in":[12,27,243],"spontaneous":[13,28,147,244],"speech":[14,29],"based":[15,231],"on":[16,60,232],"inter-syllable":[17,48,68,106,233],"boundary-based":[18,234],"prosodic":[19,75,94,128,235],"features.":[20],"Because":[21],"of":[22,55,73,96,119,126,144],"high":[24],"error":[25,209],"rate":[26,210],"recognition,":[30],"combined":[32],"acoustic":[33],"model":[34,193,198,230],"considering":[35],"both":[36],"syllable":[37],"and":[38,50,101,104,130,153,184,194,220],"subsyllable":[39],"recognition":[40,53],"units,":[41],"is":[42,82,108,169],"firstly":[43],"used":[44],"to":[45,110,138,158],"determine":[46,139],"boundaries":[49],"output":[51,111],"confidence":[54,118],"input":[57,146],"speech.":[58,148,246],"Based":[59],"finding":[62],"that":[63,176,227],"IPs":[64],"always":[65],"occur":[66],"at":[67,77],"boundaries,":[69,107],"probability":[71,124],"distribution":[72,125],"features":[76,95,129,236],"current":[79,98],"potential":[80,99],"IP":[81,100,113,133,142,161,179,207],"estimated.":[83],"The":[84,164,223],"Conditional":[85],"Random":[86],"Field":[87],"(CRF)":[88],"model,":[89],"which":[90],"employs":[91],"clustered":[93],"its":[102],"preceding":[103],"succeeding":[105],"employed":[109],"likelihood":[114,134],"measure.":[115],"Finally,":[116],"recognized":[121],"speech,":[122],"CRF-based":[132],"measure":[135],"are":[136,155],"integrated":[137],"optimal":[141],"sequence":[143],"In":[149],"addition,":[150],"pitch":[151,218],"reset":[152,219],"lengthening":[154,221],"also":[156],"applied":[157],"improve":[159],"detection":[162,180,208],"performance.":[163],"Mandarin":[165,245],"Conversional":[166],"Dialogue":[167],"Corpus":[168],"adopted":[170],"evaluation.":[172],"Experimental":[173],"results":[174,188,225],"show":[175],"proposed":[178,229],"approach":[181],"obtains":[182],"10.56%":[183],"6.5%":[185],"more":[186],"effective":[187],"than":[189],"hidden":[191],"Markov":[192],"Maximum":[196],"Entropy":[197],"respectively":[199],"under":[200],"same":[202],"experimental":[203,224],"conditions.":[204],"Besides,":[205],"can":[211,237],"be":[212],"further":[213],"reduced":[214],"by":[215],"9.15%":[216],"using":[217],"information.":[222],"confirm":[226],"effectively":[238],"detect":[239]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
