{"id":"W2095504751","doi":"10.1558/ijsll.v14i1.145","title":"Forensic automatic speaker recognition using Bayesian interpretation and statistical compensation for mismatched conditions","year":2007,"lang":"en","type":"article","venue":"International Journal of Speech Language and the Law","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Bayesian probability; Interpretation (philosophy); Computer science; Speech recognition; Speaker recognition; Compensation (psychology); Artificial intelligence; Natural language processing; Pattern recognition (psychology); Psychology; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001600987,0.0007406888,0.0008977593,0.001440822,0.0005646801,0.0009520107,0.0007714736,0.001057961,0.00380409],"category_scores_gemma":[0.005104713,0.0005924584,0.0006153039,0.0006717895,0.0004673922,0.001278056,0.00131007,0.001032777,0.002349874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003028995,"about_ca_system_score_gemma":0.001026985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008042721,"about_ca_topic_score_gemma":0.002533758,"domain_scores_codex":[0.9989261,0.0002707899,0.00005151837,0.0001937043,0.0004405762,0.0001172501],"domain_scores_gemma":[0.9983793,0.000502537,0.0001197498,0.0003007601,0.0006389763,0.00005867512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001003253,0.0001206842,0.002528811,0.0001538355,0.0000961086,0.0003295315,0.0001851376,0.03408034,0.1977349,0.009412395,0.004429802,0.7499252],"study_design_scores_gemma":[0.00004837091,0.0001437403,0.005411417,0.00004071344,0.00008429739,0.001076994,0.00006596807,0.8797266,0.09576767,0.01276589,0.004788611,0.0000797179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02295188,0.0001991427,0.9735746,0.0001299628,0.0000852478,0.0000284433,0.0001498869,0.001098839,0.001781984],"genre_scores_gemma":[0.3720437,0.0004258213,0.6214918,0.0001042311,0.0001445561,0.00007650283,0.0008777122,0.0004577971,0.004377931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00380409,"threshold_uncertainty_score":0.01272595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01824073938672336,"score_gpt":0.3016828474250562,"score_spread":0.2834421080383329,"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."}}