{"id":"W2062041088","doi":"10.1121/1.4779336","title":"Explicit pattern recognition models for speech perception","year":2003,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Classifier (UML); Perception; Speech recognition; Hidden Markov model; Bayesian probability; Pattern recognition (psychology); Set (abstract data type); Imperfect; Speech perception; Dynamic programming; Artificial intelligence; Algorithm","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":[0.000681426,0.00008796228,0.0001730123,0.00001243384,0.0001871409,0.00003751869,0.0006685603,0.00004345935,0.00001662834],"category_scores_gemma":[0.0001300658,0.00004700786,0.0003019938,0.000201793,0.0001143266,0.0002811616,0.00006876804,0.0002108296,0.000002892913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004419191,"about_ca_system_score_gemma":0.00006901989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005970091,"about_ca_topic_score_gemma":9.544736e-8,"domain_scores_codex":[0.9989927,0.00009624079,0.0002957222,0.00008567476,0.0003413144,0.000188289],"domain_scores_gemma":[0.9988147,0.0002764573,0.000386053,0.0002209389,0.0002490241,0.00005281659],"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":[0.00002931504,0.0001367296,0.00002644529,0.00005000056,0.00007264395,4.221801e-7,0.002002591,0.005438618,0.06320732,0.000008046713,0.01286562,0.9161623],"study_design_scores_gemma":[0.0008208405,0.0004977658,0.0002110148,0.0001920726,0.0001941836,0.0002535064,0.002900155,0.8659909,0.05931603,0.06850812,0.0008809261,0.0002344971],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0135736,0.00007620786,0.9826334,0.003337611,0.0001553457,0.00009528115,0.000002209019,0.000009411476,0.0001168959],"genre_scores_gemma":[0.506094,0.0001648642,0.491338,0.002261227,0.0001078672,0.000001359604,1.618198e-7,0.000006990369,0.00002553723],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9159278,"threshold_uncertainty_score":0.1916925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03095972875814961,"score_gpt":0.2613383407568129,"score_spread":0.2303786119986633,"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."}}