{"id":"W1981517541","doi":"10.1121/1.4877151","title":"Using criterio voice familiarity to augment the accuracy of speaker identification in voice lineups","year":2014,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Psychology; Speaker identification; Identification (biology); Syllable; Speech recognition; Semitone; Duration (music); Audiology; Speaker recognition; Computer science; Acoustics","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":[0.002113483,0.0001069712,0.0002636286,0.00003011616,0.0001436662,0.0000465044,0.001538881,0.00004412268,0.00001385973],"category_scores_gemma":[0.001439609,0.00005324498,0.0002755205,0.0005775753,0.0002789907,0.000178553,0.0003216389,0.0003183793,0.00000892407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007123066,"about_ca_system_score_gemma":0.00007591199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001629506,"about_ca_topic_score_gemma":0.00000341677,"domain_scores_codex":[0.9980131,0.0004565366,0.0006404103,0.000114034,0.0005912676,0.0001846723],"domain_scores_gemma":[0.9967017,0.001705938,0.0006673348,0.0005434724,0.0003145647,0.00006704926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000220405,0.0009665476,0.001000066,0.0001543295,0.0002894085,0.000001660832,0.01417777,0.02122006,0.4904034,0.0001464145,0.01704452,0.4543754],"study_design_scores_gemma":[0.0003079773,0.0001300901,0.0254983,0.0001728916,0.0001367274,0.00004462829,0.001891934,0.9501826,0.01739025,0.001702718,0.002404834,0.0001370357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1209583,0.00004630588,0.8657606,0.01276034,0.0002547793,0.0001430194,0.000002840413,0.000005856276,0.00006792109],"genre_scores_gemma":[0.8571988,0.0001051625,0.1395107,0.003032764,0.0001182576,0.000001086041,8.547693e-8,0.00000673555,0.000026368],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9289625,"threshold_uncertainty_score":0.2859648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03805159423408117,"score_gpt":0.3033150911949351,"score_spread":0.2652634969608539,"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."}}