{"id":"https://openalex.org/W1978168463","doi":"https://doi.org/10.1145/383952.384041","title":"Structure and content-based segmentation of speech transcripts","display_name":"Structure and content-based segmentation of speech transcripts","publication_year":2001,"publication_date":"2001-09-01","ids":{"openalex":"https://openalex.org/W1978168463","doi":"https://doi.org/10.1145/383952.384041","mag":"1978168463"},"language":"en","primary_location":{"id":"doi:10.1145/383952.384041","doi":"https://doi.org/10.1145/383952.384041","is_oa":false,"landing_page_url":"https://doi.org/10.1145/383952.384041","pdf_url":null,"source":{"id":"https://openalex.org/S4363608773","display_name":"International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5113463152","display_name":"Dulce B. Ponceleon","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dulce Ponceleon","raw_affiliation_strings":["IBM Almaden Research Center, San Jose, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Almaden Research Center, San Jose, CA","institution_ids":["https://openalex.org/I4210085935"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103444961","display_name":"Savitha Srinivasan","orcid":"https://orcid.org/0000-0002-6117-1159","observed_orcids":["https://orcid.org/0000-0002-6117-1159"]},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Savitha Srinivasan","raw_affiliation_strings":["IBM Almaden Research Center, San Jose, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Almaden Research Center, San Jose, CA","institution_ids":["https://openalex.org/I4210085935"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210085935"],"apc_list":null,"apc_paid":null,"fwci":3.6587,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.9292543,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"404","last_page":"405"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","score":0.635699987411499,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11439","display_name":"Video Analysis and Summarization","score":0.635699987411499,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.2167000025510788,"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":0.07190000265836716,"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/speech-segmentation","display_name":"speech segmentation","score":0.7269999980926514},{"id":"https://openalex.org/keywords/audio-segmentation","display_name":"audio segmentation","score":0.40799999237060547},{"id":"https://openalex.org/keywords/boundary-detection","display_name":"boundary detection","score":0.7239999771118164},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"information retrieval","score":0.6539999842643738},{"id":"https://openalex.org/keywords/two-pass-algorithm","display_name":"two-pass algorithm","score":0.6570000052452087},{"id":"https://openalex.org/keywords/segment-merging","display_name":"segment merging","score":0.5950000286102295}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.820866048336029},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8104358315467834},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6312451958656311},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5479289889335632},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.532585859298706},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.465984046459198},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.4460587501525879},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4024306535720825},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33357977867126465},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.15635117888450623},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/383952.384041","doi":"https://doi.org/10.1145/383952.384041","is_oa":false,"landing_page_url":"https://doi.org/10.1145/383952.384041","pdf_url":null,"source":{"id":"https://openalex.org/S4363608773","display_name":"International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W1586171079","https://openalex.org/W2008706531","https://openalex.org/W2022146129"],"related_works":["https://openalex.org/W1967983000","https://openalex.org/W180090376","https://openalex.org/W50013510","https://openalex.org/W2404380632","https://openalex.org/W2107402508","https://openalex.org/W2127285286","https://openalex.org/W2044591814","https://openalex.org/W2386807930","https://openalex.org/W2096270777","https://openalex.org/W7095861753"],"abstract_inverted_index":{"algorithm":[0,24,142],"for":[1],"the":[2,37,40,44,61,74,87,97,105,108,112,131,141,150],"segmentation":[3,98,153],"of":[4,46,48,96,107,114,130],"an":[5,57,80],"audio/video":[6],"source":[7],"into":[8],"topically":[9],"cohesive":[10],"segments":[11,89],"based":[12,63],"on":[13,104],"automatic":[14],"speech":[15],"recognition":[16],"(ASR)":[17],"transcriptions":[18],"is":[19,25,51],"presented.":[20],"A":[21],"novel":[22],"two-pass":[23],"described":[26],"that":[27],"combines":[28],"a":[29,33,126,136],"boundary-based":[30],"method":[31,122],"with":[32,145,152],"content-based":[34],"method.":[35],"In":[36,60],"first":[38],"pass,":[39,65],"temporal":[41],"proximity":[42],"and":[43,90,111,148],"rate":[45],"arrival":[47],"ngram":[49,75],"features":[50,76],"analyzed":[52],"in":[53,67,79,143],"order":[54],"to":[55],"compute":[56],"initial":[58,88],"segmentation.":[59],"content-":[62],"second":[64,84],"changes":[66],"content-bearing":[68],"words":[69],"are":[70],"detected":[71],"by":[72],"using":[73],"as":[77,93],"queries":[78],"information-retrieval":[81],"system.":[82],"The":[83],"pass":[85],"validates":[86],"merges":[91],"them":[92],"needed.":[94],"Feasibility":[95],"task":[99],"can":[100],"vary":[101],"enormously":[102],"depending":[103],"structure":[106],"audio":[109,132],"content,":[110],"accuracy":[113],"ASR.":[115],"For":[116],"real-world":[117],"corporate":[118],"training":[119],"data":[120],"our":[121],"identifies,":[123],"at":[124,134],"worst,":[125],"single":[127],"salient":[128],"segment":[129],"and,":[133],"best,":[135],"high-level":[137],"table-of-contents.":[138],"We":[139],"illustrate":[140],"detail":[144],"some":[146],"examples":[147],"validate":[149],"results":[151],"boundaries":[154],"generated":[155],"manually.":[156]},"counts_by_year":[{"year":2015,"cited_by_count":1}],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2016-06-24T00:00:00"}
