{"id":"W2170509092","doi":"10.1109/tbme.2004.842962","title":"Discrimination of Pathological Voices Using a Time-Frequency Approach","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":150,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Speech recognition; Computer science; Segmentation; Octave (electronics); Vowel; Speech processing; Noise (video); Pattern recognition (psychology); Speech enhancement; Energy (signal processing); Artificial intelligence; Mathematics; Acoustics; Noise reduction; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006582995,0.000526242,0.0005062753,0.002911371,0.0002394797,0.0007364895,0.0003127419,0.0007912193,0.001515699],"category_scores_gemma":[0.002211259,0.0001273374,0.0004741247,0.0006755779,0.0003331114,0.0008328052,0.000369226,0.0002950899,0.0008902979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001692162,"about_ca_system_score_gemma":0.0002044478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007284585,"about_ca_topic_score_gemma":0.0008563204,"domain_scores_codex":[0.999546,0.0000803271,0.00005000758,0.0001192582,0.0001533758,0.00005105502],"domain_scores_gemma":[0.9989825,0.0004640525,0.0001059278,0.00007983927,0.0002955346,0.00007213566],"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":[0.001382374,0.0002101365,0.01591506,0.0001144491,0.0000761662,0.0003039736,0.0002416866,0.005503651,0.317644,0.0007254501,0.0004407872,0.6574423],"study_design_scores_gemma":[0.000170446,0.002769093,0.2129074,0.0000690794,0.0004665575,0.006826431,0.001244715,0.615081,0.1500356,0.004986352,0.005217538,0.0002258986],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6345219,0.0005404057,0.3605312,0.0001157209,0.00009059284,0.0001652494,0.0002384876,0.0008757682,0.002920684],"genre_scores_gemma":[0.8203294,0.0003890629,0.1768638,0.00006378393,0.0000974323,0.0001247516,0.0003908488,0.00005850855,0.0016824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002911371,"threshold_uncertainty_score":0.005070448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01862899022744459,"score_gpt":0.2574048233996909,"score_spread":0.2387758331722463,"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."}}