{"id":"https://openalex.org/W2019094842","doi":"https://doi.org/10.1109/icassp.1993.319180","title":"Using parallel MLPs as labelers for multiple codebook HMMs","display_name":"Using parallel MLPs as labelers for multiple codebook HMMs","publication_year":1993,"publication_date":"1993-01-01","ids":{"openalex":"https://openalex.org/W2019094842","doi":"https://doi.org/10.1109/icassp.1993.319180","mag":"2019094842"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.1993.319180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.1993.319180","pdf_url":null,"source":{"id":"https://openalex.org/S4363607879","display_name":"IEEE International Conference on Acoustics Speech and Signal Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"IEEE International Conference on Acoustics Speech and Signal Processing","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/A5003264925","display_name":"P Le Cerf","orcid":null},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"P. Le Cerf","raw_affiliation_strings":["E.S.A.T, K.U. Leuven, Heverlee, Belgium","Katholieke Univ., Leuven, Heverlee, , Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"E.S.A.T, K.U. Leuven, Heverlee, Belgium","institution_ids":["https://openalex.org/I99464096"]},{"raw_affiliation_string":"Katholieke Univ., Leuven, Heverlee, , Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041039488","display_name":"Dirk Van Compernolle","orcid":"https://orcid.org/0000-0003-0075-848X"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"D. Van Compernolle","raw_affiliation_strings":["E.S.A.T, K.U. Leuven, Heverlee, Belgium","Katholieke Univ., Leuven, Heverlee, , Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"E.S.A.T, K.U. Leuven, Heverlee, Belgium","institution_ids":["https://openalex.org/I99464096"]},{"raw_affiliation_string":"Katholieke Univ., Leuven, Heverlee, , Belgium","institution_ids":["https://openalex.org/I99464096"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9888,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.73395445,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"561","last_page":"564 vol.1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9937000274658203,"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/T10320","display_name":"Neural Networks and Applications","score":0.9937000274658203,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9919000267982483,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9891999959945679,"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/hidden-markov-model","display_name":"Hidden Markov model","score":0.8233737349510193},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7148957848548889},{"id":"https://openalex.org/keywords/codebook","display_name":"Codebook","score":0.710369348526001},{"id":"https://openalex.org/keywords/learning-vector-quantization","display_name":"Learning vector quantization","score":0.6395584344863892},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6163291335105896},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.5400233268737793},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5368042588233948},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.5067227482795715},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4626624882221222},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.4486185312271118},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4378047287464142},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43727031350135803},{"id":"https://openalex.org/keywords/euclidean-geometry","display_name":"Euclidean geometry","score":0.43016955256462097},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3936094343662262},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.24746990203857422},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18396642804145813}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.8233737349510193},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7148957848548889},{"id":"https://openalex.org/C127759330","wikidata":"https://www.wikidata.org/wiki/Q637416","display_name":"Codebook","level":2,"score":0.710369348526001},{"id":"https://openalex.org/C40567965","wikidata":"https://www.wikidata.org/wiki/Q1820283","display_name":"Learning vector quantization","level":3,"score":0.6395584344863892},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6163291335105896},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.5400233268737793},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5368042588233948},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.5067227482795715},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4626624882221222},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.4486185312271118},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4378047287464142},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43727031350135803},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.43016955256462097},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3936094343662262},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24746990203857422},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18396642804145813},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.1993.319180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.1993.319180","pdf_url":null,"source":{"id":"https://openalex.org/S4363607879","display_name":"IEEE International Conference on Acoustics Speech and Signal Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"IEEE International Conference on Acoustics Speech and Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2106978801","https://openalex.org/W2137347187","https://openalex.org/W2140539590","https://openalex.org/W2150719436","https://openalex.org/W2170541567","https://openalex.org/W2523750004","https://openalex.org/W2525213507","https://openalex.org/W2913399920","https://openalex.org/W4244017338","https://openalex.org/W6727675915"],"related_works":["https://openalex.org/W2148772884","https://openalex.org/W2017514583","https://openalex.org/W2100120615","https://openalex.org/W2165855302","https://openalex.org/W1929869830","https://openalex.org/W156190883","https://openalex.org/W2017401491","https://openalex.org/W2387054321","https://openalex.org/W2352648934","https://openalex.org/W2145213141"],"abstract_inverted_index":{"The":[0,43],"authors":[1],"investigate":[2],"the":[3,28,40,46],"use":[4],"of":[5,24,26,45,55],"multilayer":[6],"perceptrons":[7],"(MLPs)":[8],"as":[9,51,53],"labelers":[10],"for":[11,35],"a":[12,22,56],"discrete":[13,58],"parameter":[14,37,59],"hidden":[15],"Markov":[16],"model":[17],"(HMM)":[18],"system.":[19],"They":[20],"introduce":[21],"number":[23],"strategies,":[25],"which":[27,31],"multi-MLP":[29,79],"approach,":[30],"uses":[32],"parallel":[33],"MLPs":[34],"separate":[36],"sets,":[38],"is":[39,49,81],"most":[41],"promising.":[42],"performance":[44],"new":[47],"system":[48,61],"just":[50],"good":[52],"that":[54],"classical":[57],"HMM":[60,70],"(using":[62],"multiple":[63],"Euclidean":[64,86],"vector":[65],"quantization),":[66],"but":[67],"needs":[68],"fewer":[69],"parameters":[71],"(80":[72],"compared":[73],"with":[74],"330":[75],"per":[76],"state).":[77],"Therefore,":[78],"labeling":[80],"much":[82],"more":[83],"efficient":[84],"than":[85],"labeling.<":[87],"<ETX":[88],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[89],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">&gt;</ETX>":[90]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
