{"id":"https://openalex.org/W131629047","doi":"https://doi.org/10.21437/eurospeech.1999-27","title":"Efficient vector quantization using an n-path binary tree search algorithm","display_name":"Efficient vector quantization using an n-path binary tree search algorithm","publication_year":1999,"publication_date":"1999-09-05","ids":{"openalex":"https://openalex.org/W131629047","doi":"https://doi.org/10.21437/eurospeech.1999-27","mag":"131629047"},"language":"en","primary_location":{"id":"doi:10.21437/eurospeech.1999-27","is_oa":false,"landing_page_url":"https://doi.org/10.21437/eurospeech.1999-27","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":"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/A5002427610","display_name":"Rub\u00e9n San-Segundo","orcid":"https://orcid.org/0000-0001-9659-5464"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"R. San-Segundo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067867302","display_name":"Ricardo de C\u00f3rdoba","orcid":"https://orcid.org/0000-0002-7136-9636"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"R. C\u00f3rdoba","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028386517","display_name":"Javier Ferreiros","orcid":"https://orcid.org/0000-0001-8834-3080"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"J. Ferreiros","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055871365","display_name":"A. Gallardo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"A. Gallardo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009879411","display_name":"Jaron T. Colas","orcid":"https://orcid.org/0000-0003-1872-7614"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"J. Col\u00e1s","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052220096","display_name":"Julio Pastor Mendoza","orcid":"https://orcid.org/0000-0003-0032-4446"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"J. Pastor","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5073380752","display_name":"Y. L\u00f3pez","orcid":"https://orcid.org/0009-0000-3907-1461"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Y. L\u00f3pez","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5002427610"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0183382,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"93","last_page":"96"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9997000098228455,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9997000098228455,"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/T11269","display_name":"Algorithms and Data Compression","score":0.9925000071525574,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9771000146865845,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.7016811370849609},{"id":"https://openalex.org/keywords/linde\u2013buzo\u2013gray-algorithm","display_name":"Linde\u2013Buzo\u2013Gray algorithm","score":0.694886326789856},{"id":"https://openalex.org/keywords/binary-tree","display_name":"Binary tree","score":0.6275264620780945},{"id":"https://openalex.org/keywords/binary-search-tree","display_name":"Binary search tree","score":0.6220827102661133},{"id":"https://openalex.org/keywords/optimal-binary-search-tree","display_name":"Optimal binary search tree","score":0.6050053238868713},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5614089965820312},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5554279088973999},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.5164042115211487},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5093470811843872},{"id":"https://openalex.org/keywords/learning-vector-quantization","display_name":"Learning vector quantization","score":0.4851154088973999},{"id":"https://openalex.org/keywords/self-balancing-binary-search-tree","display_name":"Self-balancing binary search tree","score":0.41531866788864136},{"id":"https://openalex.org/keywords/interval-tree","display_name":"Interval tree","score":0.21016409993171692},{"id":"https://openalex.org/keywords/tree-structure","display_name":"Tree structure","score":0.21014052629470825}],"concepts":[{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.7016811370849609},{"id":"https://openalex.org/C93372532","wikidata":"https://www.wikidata.org/wiki/Q6552455","display_name":"Linde\u2013Buzo\u2013Gray algorithm","level":3,"score":0.694886326789856},{"id":"https://openalex.org/C197855036","wikidata":"https://www.wikidata.org/wiki/Q380172","display_name":"Binary tree","level":2,"score":0.6275264620780945},{"id":"https://openalex.org/C91154448","wikidata":"https://www.wikidata.org/wiki/Q623818","display_name":"Binary search tree","level":3,"score":0.6220827102661133},{"id":"https://openalex.org/C27628562","wikidata":"https://www.wikidata.org/wiki/Q17154715","display_name":"Optimal binary search tree","level":5,"score":0.6050053238868713},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5614089965820312},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5554279088973999},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.5164042115211487},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5093470811843872},{"id":"https://openalex.org/C40567965","wikidata":"https://www.wikidata.org/wiki/Q1820283","display_name":"Learning vector quantization","level":3,"score":0.4851154088973999},{"id":"https://openalex.org/C135593954","wikidata":"https://www.wikidata.org/wiki/Q245955","display_name":"Self-balancing binary search tree","level":5,"score":0.41531866788864136},{"id":"https://openalex.org/C84927040","wikidata":"https://www.wikidata.org/wiki/Q6057306","display_name":"Interval tree","level":4,"score":0.21016409993171692},{"id":"https://openalex.org/C163797641","wikidata":"https://www.wikidata.org/wiki/Q2067937","display_name":"Tree structure","level":3,"score":0.21014052629470825},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/eurospeech.1999-27","is_oa":false,"landing_page_url":"https://doi.org/10.21437/eurospeech.1999-27","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"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.581.3994","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.581.3994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www-gth.die.upm.es/~lapiz/pub/vq99.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W125399889","https://openalex.org/W1587589763","https://openalex.org/W2002182716","https://openalex.org/W2090861223","https://openalex.org/W2137075158","https://openalex.org/W3148186152"],"related_works":["https://openalex.org/W2361906991","https://openalex.org/W2026697457","https://openalex.org/W1992432548","https://openalex.org/W2364382882","https://openalex.org/W2022598760","https://openalex.org/W2371680642","https://openalex.org/W3013722670","https://openalex.org/W4205700501","https://openalex.org/W2773551912","https://openalex.org/W2047446047"],"abstract_inverted_index":{"We":[0,68],"propose":[1],"the":[2,20,25,29,61,75,78,82,86,91,95,105,114,118,127,132,139,159,176],"utilization":[3],"of":[4,24,66,77,81,167],"a":[5,49,148,154,165],"new":[6],"n-path":[7,140],"binary":[8,141],"tree":[9,142],"search":[10,143],"algorithm":[11,33],"for":[12,64,94,104],"vector":[13,26,171],"quantization.":[14],"Our":[15,110],"target":[16],"is":[17],"to":[18,37,48,73],"reduce":[19],"complexity":[21],"(time":[22],"processing)":[23],"quantizer":[27],"maintaining":[28,175],"quantization":[30,116,172],"distortion.":[31],"The":[32],"has":[34,112],"been":[35,113],"applied":[36],"an":[38],"isolated":[39],"digit":[40],"recognizer":[41],"by":[42],"telephone":[43],"based":[44,53],"on":[45,54],"DHMM":[46],"and":[47,107,131],"continuous":[50],"speech":[51],"system":[52],"SCHMM,":[55],"so":[56],"we":[57,88,145,162],"will":[58],"also":[59],"give":[60],"recognition":[62],"results":[63],"both":[65],"them.":[67],"have":[69,89,146,163],"tested":[70],"several":[71],"alternatives":[72],"calculate":[74],"centroids":[76],"higher":[79],"levels":[80],"tree.":[83],"In":[84,123,158],"all":[85,121],"experiments":[87],"considered":[90],"following":[92],"parameters":[93],"evaluation:":[96],"average":[97,102],"distortion,":[98],"same":[99],"choice":[100],"percentage,":[101],"distortion":[103,128],"mistakes":[106],"processing":[108,133,151,173],"time.":[109],"reference":[111,125],"standard":[115],"(computing":[117],"distance":[119],"with":[120,153],"centroids).":[122],"this":[124],"case":[126],"was":[129,135],"220.9":[130],"time":[134,152],"2.1":[136],"seconds.":[137],"With":[138],"algorithm,":[144],"obtained":[147,164],"0.7":[149],"seconds":[150],"similar":[155],"distortion:":[156],"226.4.":[157],"semicontinuous":[160],"system,":[161],"reduction":[166],"71":[168],"%":[169],"in":[170],"time,":[174],"word":[177],"accuracy.":[178]},"counts_by_year":[],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
