{"id":"https://openalex.org/W1533504578","doi":"https://doi.org/10.21437/interspeech.2006-550","title":"From reaction to prediction: experiments with computational models of turn-taking","display_name":"From reaction to prediction: experiments with computational models of turn-taking","publication_year":2006,"publication_date":"2006-09-17","ids":{"openalex":"https://openalex.org/W1533504578","doi":"https://doi.org/10.21437/interspeech.2006-550","mag":"1533504578"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2006-550","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2006-550","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2006","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pub.uni-bielefeld.de/download/1992227/2265662","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032801642","display_name":"David Schlangen","orcid":"https://orcid.org/0000-0002-2686-6887"},"institutions":[{"id":"https://openalex.org/I176453806","display_name":"University of Potsdam","ror":"https://ror.org/03bnmw459","country_code":"DE","type":"education","lineage":["https://openalex.org/I176453806"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"David Schlangen","raw_affiliation_strings":["Department of Linguistics University of Potsdam, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Linguistics University of Potsdam, Germany","institution_ids":["https://openalex.org/I176453806"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5032801642"],"corresponding_institution_ids":["https://openalex.org/I176453806"],"apc_list":null,"apc_paid":null,"fwci":4.6573,"has_fulltext":true,"cited_by_count":81,"citation_normalized_percentile":{"value":0.94262769,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"paper 1200","last_page":"Wed3WeS.3"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12380","display_name":"Authorship Attribution and Profiling","score":0.9884999990463257,"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/T12380","display_name":"Authorship Attribution and Profiling","score":0.9884999990463257,"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/T12031","display_name":"Speech and dialogue systems","score":0.9865999817848206,"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/T10383","display_name":"Language, Discourse, Communication Strategies","score":0.9818000197410583,"subfield":{"id":"https://openalex.org/subfields/1203","display_name":"Language and Linguistics"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6393352746963501},{"id":"https://openalex.org/keywords/turn","display_name":"Turn (biochemistry)","score":0.5673097968101501},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06605291366577148}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6393352746963501},{"id":"https://openalex.org/C85641259","wikidata":"https://www.wikidata.org/wiki/Q290042","display_name":"Turn (biochemistry)","level":2,"score":0.5673097968101501},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06605291366577148},{"id":"https://openalex.org/C46141821","wikidata":"https://www.wikidata.org/wiki/Q209402","display_name":"Nuclear magnetic resonance","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/interspeech.2006-550","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2006-550","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2006","raw_type":"proceedings-article"},{"id":"pmh:oai:pub.uni-bielefeld.de:1992227","is_oa":true,"landing_page_url":"https://pub.uni-bielefeld.de/record/1992227","pdf_url":"https://pub.uni-bielefeld.de/download/1992227/2265662","source":{"id":"https://openalex.org/S4306401624","display_name":"Publikationen an der Universit\u00e4t Bielefeld (Universit\u00e4t Bielefeld)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I20121455","host_organization_name":"Bielefeld University","host_organization_lineage":["https://openalex.org/I20121455"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Schlangen D. From Reaction to Prediction: Experiments with Computational Models of Turn-Taking. In:  &lt;em&gt;Proceedings of Interspeech 2006, Panel on Prosody of Dialogue Acts and Turn-Taking&lt;/em&gt;. Pittsburgh, USA;  2006.","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:pub.uni-bielefeld.de:1992227","is_oa":true,"landing_page_url":"https://pub.uni-bielefeld.de/record/1992227","pdf_url":"https://pub.uni-bielefeld.de/download/1992227/2265662","source":{"id":"https://openalex.org/S4306401624","display_name":"Publikationen an der Universit\u00e4t Bielefeld (Universit\u00e4t Bielefeld)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I20121455","host_organization_name":"Bielefeld University","host_organization_lineage":["https://openalex.org/I20121455"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Schlangen D. From Reaction to Prediction: Experiments with Computational Models of Turn-Taking. In:  &lt;em&gt;Proceedings of Interspeech 2006, Panel on Prosody of Dialogue Acts and Turn-Taking&lt;/em&gt;. Pittsburgh, USA;  2006.","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1533504578.pdf","grobid_xml":"https://content.openalex.org/works/W1533504578.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W68208136","https://openalex.org/W1499881690","https://openalex.org/W1549285799","https://openalex.org/W1566669007","https://openalex.org/W1570448133","https://openalex.org/W1583748115","https://openalex.org/W1632114991","https://openalex.org/W1875231349","https://openalex.org/W1903951673","https://openalex.org/W1973731132","https://openalex.org/W2034811676","https://openalex.org/W2153190547","https://openalex.org/W2166637769","https://openalex.org/W2237495196","https://openalex.org/W2426479676"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4231070408","https://openalex.org/W943151747","https://openalex.org/W2048981943","https://openalex.org/W2753901553","https://openalex.org/W2804368879","https://openalex.org/W4245567755","https://openalex.org/W2325174796"],"abstract_inverted_index":{"Deciding":[0],"when":[1],"to":[2,6,34,52,65,121,145],"take":[3],"(or":[4],"not":[5],"take)":[7],"the":[8,21,127,139,147],"turn":[9],"in":[10,20],"a":[11,38,50,88,96,103],"conversation":[12],"is":[13,48,143],"an":[14,70],"important":[15],"task.It":[16],"has":[17,41],"been":[18,42],"stressed":[19],"descriptive":[22],"literature":[23],"that":[24,63,75,87,156],"such":[25],"decisions":[26,83],"must":[27],"involve":[28],"prediction,":[29],"as":[30],"they":[31],"often":[32],"seem":[33],"be":[35,146],"made":[36],"before":[37],"transition":[39],"place":[40],"reached.In":[43],"computational":[44],"systems,":[45],"however,":[46],"turn-taking":[47,82,123],"normally":[49],"reaction":[51,131],"parameters":[53],"like":[54],"pause":[55,114,128],"length.In":[56],"this":[57,67],"paper,":[58],"we":[59,112,151],"report":[60],"on":[61,107],"experiments":[62,106],"try":[64],"bridge":[66],"gap.We":[68],"describe":[69,102],"experiment":[71],"(using":[72],"controlled":[73],"stimuli)":[74],"shows":[76],"human":[77],"performance":[78],"at":[79],"prediction":[80,141],"of":[81,99,105],"and":[84,118,150],"then":[85,101],"show":[86],"model":[89],"automatically":[90],"induced":[91],"from":[92],"data":[93,110],"can":[94],"reach":[95],"similar":[97],"level":[98],"performance.We":[100],"series":[104],"spontaneous":[108],"dialogue":[109],"where":[111],"combine":[113],"thresholds":[115],"with":[116],"syntactic":[117],"prosodic":[119],"information":[120,154],"make":[122],"decisions,":[124],"successively":[125],"reducing":[126],"threshold":[129],"until":[130],"becomes":[132],"prediction.All":[133],"our":[134],"classifiers":[135],"improve":[136,158],"significantly":[137],"over":[138],"baselines;":[140],"however":[142],"shown":[144],"hardest":[148],"task,":[149],"discuss":[152],"additional":[153],"sources":[155],"could":[157],"it.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":6},{"year":2013,"cited_by_count":9},{"year":2012,"cited_by_count":6}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
