{"id":"W2136207028","doi":"10.1109/imtc.2008.4547192","title":"Security Instrument using Talker Identification and Microphone Arrays in Variable Noisy Environments","year":2008,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Microphone; Computer science; Ranging; Noise (video); Identification (biology); Speech recognition; Estimator; Signal-to-noise ratio (imaging); Classifier (UML); Pattern recognition (psychology); Acoustics; Artificial intelligence; Mathematics; Telecommunications; Statistics","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":[0.0001339717,0.00007770066,0.0000836398,0.00006613554,0.0001237036,0.00007614718,0.0001725933,0.00003536453,0.00001677438],"category_scores_gemma":[0.000006264746,0.00007592746,0.0000101953,0.0002141459,0.00004006388,0.0005532506,0.0001241351,0.00006535032,0.00001699145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005910996,"about_ca_system_score_gemma":0.00003094071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008200433,"about_ca_topic_score_gemma":0.000004190949,"domain_scores_codex":[0.9992262,0.00001845721,0.0001689561,0.0002777288,0.0001385651,0.0001700565],"domain_scores_gemma":[0.9996873,0.000008077441,0.00005642982,0.0001933735,0.000006783646,0.00004804988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000255623,0.0001019741,0.009242349,0.00001189321,0.000004529334,0.00001476822,0.0008112102,0.00016934,0.985393,0.0001592834,0.00006258656,0.004026533],"study_design_scores_gemma":[0.0004815602,0.00001432054,0.01701591,0.00002377131,0.00000226692,0.0001052685,0.00004195263,0.01478887,0.9641549,0.002269579,0.0009088609,0.0001927117],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6551238,0.00007405039,0.3442186,0.00006489758,0.0000689294,0.00005322201,4.026909e-7,0.00001669275,0.0003794099],"genre_scores_gemma":[0.8567021,0.00004064336,0.1429435,0.0001290364,0.00001415051,0.000002403429,0.000001162905,0.000003474385,0.0001635411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2015783,"threshold_uncertainty_score":0.3096232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01559576238408877,"score_gpt":0.2111121254086997,"score_spread":0.195516363024611,"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."}}