{"id":"W1960168474","doi":"10.1109/icassp.1983.1172102","title":"A comparison of distance measures for text-independent speaker identification","year":2005,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Mahalanobis distance; Distance measures; Measure (data warehouse); Pattern recognition (psychology); k-nearest neighbors algorithm; Artificial intelligence; A priori and a posteriori; Earth mover's distance; Speech recognition; Maximum a posteriori estimation; Distance measurement; Correlation; Mathematics; Statistics; Computer science; Maximum likelihood; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002788961,0.00006304005,0.0001274471,0.00005843296,0.00004880465,0.00006602779,0.0003423837,0.00003029942,0.00008349793],"category_scores_gemma":[0.00006628457,0.00005455656,0.00006845772,0.0001149135,0.00001888102,0.00024517,0.00002616033,0.00002905184,0.00008634879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002617545,"about_ca_system_score_gemma":0.0000183411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005170929,"about_ca_topic_score_gemma":0.000121298,"domain_scores_codex":[0.9991278,0.00002259806,0.0002879924,0.0001994843,0.0002491023,0.0001130048],"domain_scores_gemma":[0.9993434,0.00008764648,0.0001129354,0.0002746748,0.0001407926,0.0000405264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009426805,0.0001987176,0.001144346,0.00001166763,0.00001379268,1.374122e-7,0.0003461131,0.00001784099,0.007140703,0.04611273,0.004332814,0.9406717],"study_design_scores_gemma":[0.0005028442,0.00004506891,0.01475245,0.00002163352,0.00001481856,0.000003736614,0.0001975406,0.1639341,0.7079446,0.003229822,0.109118,0.0002354149],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005087016,0.0000766217,0.9870688,0.001071158,0.0001061655,0.0001752458,0.000003509377,0.00007078533,0.006340742],"genre_scores_gemma":[0.8934108,0.000003572891,0.1051089,0.0000926893,0.00003537639,0.00002202545,0.000001720206,0.000003526372,0.001321431],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9404363,"threshold_uncertainty_score":0.2224752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05779273137923745,"score_gpt":0.3270319084667389,"score_spread":0.2692391770875014,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}