{"id":"W2003022189","doi":"10.1109/icassp.2002.5745497","title":"Discrimination of pathological voices using an adaptive time-frequency approach","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Acoustics Speech and Signal Processing","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Toronto Metropolitan University","funders":"","keywords":"Octave (electronics); Speech recognition; Computer science; Energy (signal processing); SIGNAL (programming language); Function (biology); Speech processing; Pattern recognition (psychology); Artificial intelligence; Acoustics; Mathematics; 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":[0.0001556398,0.0001774358,0.0002503517,0.0001749755,0.00009655677,0.0000753493,0.0001409929,0.0001255942,0.0003143607],"category_scores_gemma":[0.00005456761,0.0001470846,0.00004667255,0.0001095392,0.000183075,0.0003121458,0.00001931139,0.0002389889,0.000008208774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004565169,"about_ca_system_score_gemma":0.00006562808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001713992,"about_ca_topic_score_gemma":0.000001480903,"domain_scores_codex":[0.9986588,0.00003642795,0.0002993768,0.0003220364,0.0004991497,0.0001842181],"domain_scores_gemma":[0.9990867,0.00003199055,0.000179662,0.00009539731,0.0004921197,0.0001141411],"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":[0.0004422763,0.002119767,0.001322254,0.0002996334,0.00009334161,0.0001352935,0.001691345,0.0009853368,0.8456572,0.002670254,0.00008652478,0.1444967],"study_design_scores_gemma":[0.0007256336,0.0009149389,0.0008434133,0.0003245595,0.0001057939,0.0001455238,0.001526341,0.9878873,0.004316632,0.002993166,0.000003798709,0.0002128694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8730817,0.0001633114,0.09613613,0.0002588823,0.00007646394,0.0002328303,0.00003196565,0.00005303057,0.02996564],"genre_scores_gemma":[0.9739364,0.00007378291,0.02531707,0.0001770552,0.0001409184,0.000004527466,0.00002958594,0.00001468675,0.0003059201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.986902,"threshold_uncertainty_score":0.5997936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1223920694671383,"score_gpt":0.3291479886963724,"score_spread":0.2067559192292341,"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."}}