{"id":"https://openalex.org/W134567055","doi":"https://doi.org/10.21437/eurospeech.1999-548","title":"A language model combining n-grams and stochastic finite state automata","display_name":"A language model combining n-grams and stochastic finite state automata","publication_year":1999,"publication_date":"1999-09-05","ids":{"openalex":"https://openalex.org/W134567055","doi":"https://doi.org/10.21437/eurospeech.1999-548","mag":"134567055"},"language":"en","primary_location":{"id":"doi:10.21437/eurospeech.1999-548","is_oa":false,"landing_page_url":"https://doi.org/10.21437/eurospeech.1999-548","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"6th European Conference on Speech Communication and Technology","raw_type":"proceedings-article"},"type":"conference-paper","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/A5028541034","display_name":"Alexis Nasr","orcid":"https://orcid.org/0000-0001-7573-3282"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alexis Nasr","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081852717","display_name":"Yannick Est\u00e8ve","orcid":"https://orcid.org/0000-0002-3656-8883"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yannick Est\u00e9ve","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110498175","display_name":"Fr\u00e9d\u00e9ric B\u00e9chet","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fr\u00e9d\u00e9ric B\u00e9chet","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012749011","display_name":"Thierry Spriet","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thierry Spriet","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5081408760","display_name":"Renato De Mori","orcid":"https://orcid.org/0000-0002-2083-9095"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Renato de Mori","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2175","last_page":"2178"},"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":0.9972000122070312,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9769999980926514,"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/adaptation","display_name":"Adaptation (eye)","score":0.7643610239028931},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7346776127815247},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6131752133369446},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5696401596069336},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5683388113975525},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.5524743795394897},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5511457920074463},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5328377485275269},{"id":"https://openalex.org/keywords/prior-information","display_name":"Prior information","score":0.41311848163604736},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.410768061876297},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.36021119356155396},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35873714089393616},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.2921549081802368},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19884389638900757},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18167683482170105}],"concepts":[{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.7643610239028931},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7346776127815247},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6131752133369446},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5696401596069336},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5683388113975525},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.5524743795394897},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5511457920074463},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5328377485275269},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.41311848163604736},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.410768061876297},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.36021119356155396},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35873714089393616},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.2921549081802368},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19884389638900757},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18167683482170105},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/eurospeech.1999-548","is_oa":false,"landing_page_url":"https://doi.org/10.21437/eurospeech.1999-548","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"6th European Conference on Speech Communication and Technology","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5699999928474426}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1988520084","https://openalex.org/W2079819958","https://openalex.org/W2100969003","https://openalex.org/W2146871184","https://openalex.org/W2151945664","https://openalex.org/W2333269907","https://openalex.org/W2484208911","https://openalex.org/W2974222084","https://openalex.org/W3030678767"],"related_works":["https://openalex.org/W1839961359","https://openalex.org/W2075146114","https://openalex.org/W2562263695","https://openalex.org/W2123454964","https://openalex.org/W2135187896","https://openalex.org/W2767026009","https://openalex.org/W2150865841","https://openalex.org/W2160108762","https://openalex.org/W2015518264","https://openalex.org/W2147201983"],"abstract_inverted_index":{"Maximum":[0],"a":[1,13,18,24,34,50,75,89],"posteriori":[2],"adaptation":[3,10,26,94,126,146],"method":[4],"combines":[5],"the":[6,61,98,106,131],"prior":[7,38,62,90,99,116,133],"knowledge":[8],"with":[9,142],"data":[11,70,127],"from":[12,67],"new":[14,107],"speaker,":[15],"which":[16],"has":[17,23],"nice":[19],"asymptotical":[20],"property,":[21],"but":[22],"slow":[25],"rate":[27],"for":[28],"not":[29],"modifying":[30],"unseen":[31,112],"models.":[32],"In":[33,84],"strictly":[35],"Bayesian":[36,58],"approach,":[37,59],"parameters":[39,63,100,137],"are":[40,64],"assumed":[41],"known,":[42],"based":[43],"on":[44,130],"common":[45],"or":[46],"subjective":[47],"knowledge.":[48],"But":[49],"practical":[51],"solution":[52],"is":[53,74],"to":[54,101,123],"adopt":[55],"an":[56],"empirical":[57],"where":[60],"estimated":[65],"directly":[66],"training":[68,80],"speech":[69],"itself.":[71],"So":[72],"there":[73],"problem":[76],"of":[77,105,125,145],"mismatches":[78],"between":[79],"and":[81],"testing":[82],"conditions.":[83],"this":[85],"paper":[86],"we":[87],"propose":[88],"parameter":[91,117],"transformation":[92],"(PPT)":[93],"approach":[95],"that":[96],"transforms":[97],"be":[102,139],"more":[103],"representative":[104],"speaker.":[108],"It":[109],"can":[110,138],"influence":[111],"models":[113,121],"by":[114],"tying":[115],"transformations":[118],"across":[119],"different":[120],"according":[122],"amount":[124,144],"available.":[128],"Based":[129],"improved":[132],"information":[134],"better":[135],"model":[136],"obtained":[140],"even":[141],"small":[143],"data.":[147]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2016-06-24T00:00:00"}