{"id":"https://openalex.org/W51485825","doi":"https://doi.org/10.21437/interspeech.2006-512","title":"Prosodic features for a maximum entropy language model","display_name":"Prosodic features for a maximum entropy language model","publication_year":2006,"publication_date":"2006-09-17","ids":{"openalex":"https://openalex.org/W51485825","doi":"https://doi.org/10.21437/interspeech.2006-512","mag":"51485825"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2006-512","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2006-512","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":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102259245","display_name":"Oscar Siu\u2010Hong Chan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Oscar Chan","raw_affiliation_strings":["School of Electrical, Electronic and Computer Engineering"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Electronic and Computer Engineering","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017213156","display_name":"Roberto Togneri","orcid":"https://orcid.org/0000-0002-3778-4633"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roberto Togneri","raw_affiliation_strings":["School of Electrical, Electronic and Computer Engineering"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Electronic and Computer Engineering","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102259245"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3972,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.83527848,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"paper 1150","last_page":"Wed2CaP.2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9955999851226807,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9955999851226807,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9945999979972839,"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":0.9940999746322632,"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/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.7055966258049011},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6266198754310608},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5019714832305908},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.43762046098709106},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.42739206552505493},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4203068017959595},{"id":"https://openalex.org/keywords/thermodynamics","display_name":"Thermodynamics","score":0.0950893759727478},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07551363110542297}],"concepts":[{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.7055966258049011},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6266198754310608},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5019714832305908},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.43762046098709106},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.42739206552505493},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4203068017959595},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0950893759727478},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07551363110542297}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.21437/interspeech.2006-512","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2006-512","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:pure.atira.dk:publications/38b61607-f806-4d87-8ca6-d4c6b70ea96f","is_oa":false,"landing_page_url":"https://research-repository.uwa.edu.au/en/publications/38b61607-f806-4d87-8ca6-d4c6b70ea96f","pdf_url":null,"source":{"id":"https://openalex.org/S4306402523","display_name":"UWA Profiles and Research Repository (University of Western Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I177877127","host_organization_name":"The University of Western Australia","host_organization_lineage":["https://openalex.org/I177877127"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Chan, O & Togneri, R 2006, Prosodic Features for a Maximum Entropy Language Model. in Proceedings of the Ninth International Conference on Spoken Language Processing (Interspeech 2006 - ICSLP). Pittsburgh, Pennsylvania USA edn, vol. NA, ISCA, c/o Institut fur Kommunikationswissenschaften, Universitat Bonn, Bonn, Germany, pp. 1858-1861.","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:pure.atira.dk:publications/caa2ae59-8e79-4d51-82fd-87fb2d55b12f","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306402492","display_name":"UWA Profiles and Research Repository (UWA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I177877127","host_organization_name":"The University of Western Australia","host_organization_lineage":["https://openalex.org/I177877127"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:pure.atira.dk:publications/caa2ae59-8e79-4d51-82fd-87fb2d55b12f","is_oa":false,"landing_page_url":"https://research-repository.uwa.edu.au/en/publications/caa2ae59-8e79-4d51-82fd-87fb2d55b12f","pdf_url":null,"source":{"id":"https://openalex.org/S4306402523","display_name":"UWA Profiles and Research Repository (University of Western Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I177877127","host_organization_name":"The University of Western Australia","host_organization_lineage":["https://openalex.org/I177877127"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Chan, O 2008, 'Prosodic features for a maximum entropy language model', Doctor of Philosophy.","raw_type":"book"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5099999904632568}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W162654330","https://openalex.org/W203785280","https://openalex.org/W1563235770","https://openalex.org/W1578099820","https://openalex.org/W1797231893","https://openalex.org/W1932968309","https://openalex.org/W1984635093","https://openalex.org/W2153271982","https://openalex.org/W2154872931","https://openalex.org/W2156718197","https://openalex.org/W2169658215"],"related_works":["https://openalex.org/W2169518243","https://openalex.org/W3101314311","https://openalex.org/W2351977092","https://openalex.org/W3188962172","https://openalex.org/W2108408304","https://openalex.org/W2772917594","https://openalex.org/W3204019825","https://openalex.org/W2047632477","https://openalex.org/W3041490575","https://openalex.org/W2970690932"],"abstract_inverted_index":{"A":[0],"statistical":[1,217],"language":[2,13,38,112,136,151],"model":[3,137,152,180,266],"attempts":[4],"to":[5,71,79,101,109,142,166,195,307],"characterise":[6],"the":[7,47,63,111,132,135,157,179,196,209,216,231,241,265,268,280,284],"patterns":[8],"present":[9],"in":[10,84,90,206],"a":[11,15,31,116,203,221,290,296],"natural":[12],"as":[14,42,72],"probability":[16],"distribution":[17],"defined":[18],"over":[19,128],"word":[20,27,197,310],"sequences.":[21],"Typically,":[22],"they":[23],"are":[24,124,154,276],"trained":[25],"using":[26,252,317],"co-occurrence":[28],"statistics":[29],"from":[30,170,240],"large":[32,222],"sample":[33],"of":[34,49,56,68,97,115,224,233,247,282],"text.":[35],"In":[36],"some":[37],"modelling":[39,113,167],"applications,":[40],"such":[41,144],"automatic":[43],"speech":[44,69,86,118,242],"recognition":[45,119,198],"(ASR),":[46],"availability":[48],"acoustic":[50],"data":[51],"provides":[52,162],"an":[53,81,139,163,248],"additional":[54],"source":[55],"knowledge.":[57],"This":[58],"contains,":[59],"amongst":[60],"other":[61],"things,":[62],"melodic":[64],"and":[65,149,186,191,256,309,321],"rhythmic":[66],"aspects":[67],"referred":[70],"prosody.":[73],"Although":[74],"prosody":[75],"has":[76,92,202],"been":[77,93],"found":[78],"be":[80,107,237,261,314],"important":[82],"factor":[83],"human":[85,212],"recognition,":[87],"its":[88],"use":[89],"ASR":[91],"limited.":[94],"The":[95,146],"goal":[96],"this":[98],"research":[99],"is":[100,138],"investigate":[102],"how":[103],"prosodic":[104,122,147,250],"information":[105,168],"can":[106,236,260,313],"employed":[108],"improve":[110],"component":[114],"continuous":[117],"system.":[120],"Because":[121],"features":[123,148,153,177,234],"largely":[125],"suprasegmental,":[126],"operating":[127],"units":[129],"larger":[130],"than":[131],"phonetic":[133],"segment,":[134],"appropriate":[140],"place":[141],"incorporate":[143],"information.":[145],"standard":[150],"combined":[155],"under":[156],"maximum":[158,291],"entropy":[159,292],"framework,":[160],"which":[161,235,259],"elegant":[164],"solution":[165],"obtained":[169,315],"multiple,":[171],"differing":[172],"knowledge":[173],"sources.":[174],"We":[175,227,244],"derive":[176],"for":[178,211,220,270],"based":[181],"on":[182,295],"perceptually":[183],"transcribed":[184,298,320],"Tones":[185],"Break":[187],"Indices":[188],"(ToBI)":[189],"labels,":[190],"analyse":[192],"their":[193],"contribution":[194],"task.":[199],"While":[200],"ToBI":[201],"solid":[204],"foundation":[205],"linguistic":[207],"theory,":[208],"need":[210,269],"transcribers":[213],"conflicts":[214],"with":[215,279,288],"model's":[218],"requirement":[219],"quantity":[223],"training":[225,289],"data.":[226],"therefore":[228],"also":[229,277],"examine":[230],"applicability":[232],"automatically":[238,322],"extracted":[239,323],"signal.":[243],"develop":[245],"representations":[246],"utterance's":[249],"context":[251],"fundamental":[253],"frequency,":[254],"energy":[255],"duration":[257],"features,":[258],"directly":[262],"incorporated":[263],"into":[264],"without":[267],"manual":[271],"labelling.":[272],"Dimensionality":[273],"reduction":[274],"techniques":[275],"explored":[278],"aim":[281],"reducing":[283],"computational":[285],"costs":[286],"associated":[287],"model.":[293],"Experiments":[294],"prosodically":[297],"corpus":[299],"show":[300],"that":[301],"small":[302],"but":[303],"statistically":[304],"significant":[305],"reductions":[306],"perplexity":[308],"error":[311],"rates":[312],"by":[316],"both":[318],"manually":[319],"features.":[324]},"counts_by_year":[{"year":2015,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
