{"id":"W2765668343","doi":"10.1109/atsip.2017.8075567","title":"Automatic detection of early stages of Parkinson's disease through acoustic voice analysis with mel-frequency cepstral coefficients","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Mel-frequency cepstrum; Speech recognition; Cepstrum; Computer science; Voice analysis; Feature extraction; Artificial intelligence","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":[0.0008234073,0.0007366356,0.000519622,0.001447465,0.0001505661,0.000630378,0.0002327805,0.0005043621,0.001480223],"category_scores_gemma":[0.001815092,0.0001585901,0.0002848225,0.0003831133,0.0001807134,0.0003690909,0.0003689741,0.0002862773,0.001284602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007455744,"about_ca_system_score_gemma":0.0001901396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000761651,"about_ca_topic_score_gemma":0.001168792,"domain_scores_codex":[0.999473,0.0001548987,0.0000418593,0.000154991,0.0001163585,0.00005886905],"domain_scores_gemma":[0.9993533,0.0003227829,0.00006969753,0.00005584897,0.0001658074,0.00003249609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001216062,0.000221606,0.0384232,0.0003936011,0.0001769912,0.0005498829,0.0005532892,0.002551668,0.2912253,0.0005346898,0.002989622,0.6611641],"study_design_scores_gemma":[0.00009107202,0.00121027,0.6654717,0.0001538981,0.0004490784,0.005432844,0.0008960994,0.1282209,0.1852024,0.001968389,0.01073559,0.000167737],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6857601,0.003032777,0.302559,0.0001633439,0.0001339443,0.0002565373,0.001877155,0.00205681,0.004160396],"genre_scores_gemma":[0.8921731,0.001102785,0.1023133,0.00003877683,0.00008252792,0.0001207403,0.001423796,0.0001391466,0.00260593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001480223,"threshold_uncertainty_score":0.004951835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02056211619667951,"score_gpt":0.2963418213256857,"score_spread":0.2757797051290062,"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."}}