{"id":"https://openalex.org/W2114706887","doi":"https://doi.org/10.1109/icassp.1986.1168881","title":"Evaluation of a vector quantization talker recognition system in text independent and text dependent modes","display_name":"Evaluation of a vector quantization talker recognition system in text independent and text dependent modes","publication_year":2005,"publication_date":"2005-03-23","ids":{"openalex":"https://openalex.org/W2114706887","doi":"https://doi.org/10.1109/icassp.1986.1168881","mag":"2114706887"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.1986.1168881","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.1986.1168881","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing","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/A5025786251","display_name":"A. E. Rosenberg","orcid":null},"institutions":[{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]},{"id":"https://openalex.org/I72090969","display_name":"Nokia (United States)","ror":"https://ror.org/038km2573","country_code":"US","type":"company","lineage":["https://openalex.org/I2738502077","https://openalex.org/I72090969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"A. Rosenberg","raw_affiliation_strings":["Speech Research Department, AT and T Bell Laboratories, Inc., Murray Hill, NJ, USA","AT&T Bell Laboratories,, Murray Hill, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Speech Research Department, AT and T Bell Laboratories, Inc., Murray Hill, NJ, USA","institution_ids":["https://openalex.org/I1283103587"]},{"raw_affiliation_string":"AT&T Bell Laboratories,, Murray Hill, NJ, USA","institution_ids":["https://openalex.org/I72090969"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065394791","display_name":"Frank K. Soong","orcid":"https://orcid.org/0000-0002-9088-3577"},"institutions":[{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]},{"id":"https://openalex.org/I73544541","display_name":"Mathematical Sciences Research Institute","ror":"https://ror.org/05hs5r386","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I73544541"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"F. Soong","raw_affiliation_strings":["Speech Research Department, AT and T Bell Laboratories, Inc., Murray Hill, NJ, USA","Speech Research Department, AT&T Bell Laboratories, Murrav Hill, New Jersey, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Speech Research Department, AT and T Bell Laboratories, Inc., Murray Hill, NJ, USA","institution_ids":["https://openalex.org/I1283103587"]},{"raw_affiliation_string":"Speech Research Department, AT&T Bell Laboratories, Murrav Hill, New Jersey, U.S.A","institution_ids":["https://openalex.org/I73544541"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":null,"first_page":"873","last_page":"876"},"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9987999796867371,"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":0.9987000226974487,"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/vector-quantization","display_name":"Vector quantization","score":0.8239319920539856},{"id":"https://openalex.org/keywords/codebook","display_name":"Codebook","score":0.8190097808837891},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7395188808441162},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.7201874256134033},{"id":"https://openalex.org/keywords/linde\u2013buzo\u2013gray-algorithm","display_name":"Linde\u2013Buzo\u2013Gray algorithm","score":0.6368474960327148},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.5909491181373596},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5697500109672546},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.49950599670410156},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4984004497528076},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.4926643967628479},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3888591527938843},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3734080195426941},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.26254796981811523},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16977950930595398}],"concepts":[{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.8239319920539856},{"id":"https://openalex.org/C127759330","wikidata":"https://www.wikidata.org/wiki/Q637416","display_name":"Codebook","level":2,"score":0.8190097808837891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7395188808441162},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7201874256134033},{"id":"https://openalex.org/C93372532","wikidata":"https://www.wikidata.org/wiki/Q6552455","display_name":"Linde\u2013Buzo\u2013Gray algorithm","level":3,"score":0.6368474960327148},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.5909491181373596},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5697500109672546},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.49950599670410156},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4984004497528076},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.4926643967628479},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3888591527938843},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3734080195426941},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26254796981811523},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16977950930595398},{"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.1986.1168881","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.1986.1168881","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6800000071525574,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1637097092","https://openalex.org/W1791154168","https://openalex.org/W1945356021","https://openalex.org/W1976034612","https://openalex.org/W2002182716","https://openalex.org/W2028088067","https://openalex.org/W2046316417","https://openalex.org/W2057833190","https://openalex.org/W2134383396","https://openalex.org/W2167499933","https://openalex.org/W2585529150","https://openalex.org/W6657584863"],"related_works":["https://openalex.org/W2017514583","https://openalex.org/W2352648934","https://openalex.org/W2391875658","https://openalex.org/W2100120615","https://openalex.org/W1929869830","https://openalex.org/W2017401491","https://openalex.org/W2387054321","https://openalex.org/W2158612577","https://openalex.org/W2148772884","https://openalex.org/W1681061148"],"abstract_inverted_index":{"A":[0],"vector":[1,26],"quantization":[2,27],"based":[3,14],"talker":[4,76],"recognition":[5],"system":[6,12,38,62],"is":[7,13,34,93],"described":[8],"and":[9,49,88],"evaluated.":[10],"The":[11,61],"on":[15],"constructing":[16],"highly":[17],"efficient":[18],"short-term":[19],"spectral":[20],"representations":[21],"of":[22,70,83],"individual":[23],"talkers":[24],"using":[25,66],"codebook":[28],"construction":[29],"techniques.":[30],"Although":[31],"the":[32,37],"approach":[33],"intrinsically":[35],"text-independent,":[36],"can":[39],"be":[40],"easily":[41],"extended":[42],"to":[43,57],"text-dependent":[44,91],"operation":[45,87,92],"for":[46,85,90,95],"improved":[47],"performance":[48,82],"security":[50],"by":[51],"encoding":[52],"specified":[53],"training":[54],"word":[55,59],"utterances":[56],"form":[58],"prototypes.":[60],"has":[63],"been":[64],"evaluated":[65],"a":[67,75],"100-talker":[68],"database":[69],"20,000":[71],"spoken":[72],"digits.":[73],"In":[74],"verification":[77],"mode,":[78],"average":[79],"equal-error":[80],"rate":[81],"2.2%":[84],"text-independent":[86],"0.3%":[89],"obtained":[94],"7-digit":[96],"long":[97],"test":[98],"utterances.":[99]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
