{"id":"W4391885575","doi":"10.32920/25234627","title":"Non-Linear and Non-Stationary Speech Analysis of Parkinson’s Disease Using Empirical Mode Decomposition","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg; University of Manitoba; Brock University","funders":"Universidad de Antioquia","keywords":"Speech recognition; Discriminative model; Hilbert–Huang transform; Set (abstract data type); Dysarthria; Computer science; Parkinson's disease; Phonation; Artificial intelligence; Vowel; Feature (linguistics); Pattern recognition (psychology); Audiology; Filter (signal processing); Disease; Medicine","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":[0.0001277091,0.0002460645,0.0006617544,0.0007690676,0.00003867051,0.00002639877,0.00007215812,0.0001988033,0.0001582397],"category_scores_gemma":[0.00002255843,0.000215377,0.0003848253,0.0004953747,0.00006789291,0.00003918498,0.0002996842,0.0003899087,0.00001130193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009044417,"about_ca_system_score_gemma":0.0003541227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005065614,"about_ca_topic_score_gemma":0.0001050489,"domain_scores_codex":[0.9984223,0.00002362386,0.0004438507,0.000553467,0.0003840936,0.0001726652],"domain_scores_gemma":[0.9990678,0.00004789391,0.0001092435,0.0003923591,0.000148237,0.0002343993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001324627,0.001377987,0.8821163,0.00596199,0.01106631,0.0008455764,0.002108495,0.0810579,0.003434391,0.00006049519,0.004934676,0.005711245],"study_design_scores_gemma":[0.000360674,0.00004001244,0.1374862,0.0003056137,0.008910882,0.000005403292,0.0001802485,0.8512341,0.0002546342,0.0009093901,0.0001107544,0.0002020726],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883141,0.0004996201,0.00772041,0.001555536,0.0001158029,0.0004268807,0.0001749775,0.00004637739,0.001146275],"genre_scores_gemma":[0.9800091,0.0002929928,0.01755058,0.0004970338,0.0001117351,0.00001506459,0.001149706,0.0000316642,0.0003420831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7701762,"threshold_uncertainty_score":0.8782821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03810561262028916,"score_gpt":0.4164186159751895,"score_spread":0.3783130033549003,"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."}}