{"id":"W2093148957","doi":"10.1109/iembs.2011.6091793","title":"Telephone-quality pathological speech classification using empirical mode decomposition","year":2011,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hilbert–Huang transform; Speech recognition; Computer science; Classifier (UML); Speech processing; Support vector machine; Artificial intelligence; Pattern recognition (psychology); Speech enhancement; Noise reduction; White noise; Telecommunications","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":[0.0008078727,0.0004809709,0.0004166438,0.001187424,0.0001598494,0.0004796133,0.0003375169,0.0004843414,0.001199822],"category_scores_gemma":[0.002436779,0.0001417167,0.0003704537,0.000346217,0.0003371424,0.0005577437,0.0003691046,0.0004335158,0.0005926942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001450974,"about_ca_system_score_gemma":0.0002155608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004504437,"about_ca_topic_score_gemma":0.0005690362,"domain_scores_codex":[0.9995486,0.0001295349,0.00003089702,0.00008210151,0.0001827322,0.0000260793],"domain_scores_gemma":[0.9992601,0.0003248997,0.00008364766,0.0001139608,0.0001822805,0.00003510972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004410727,0.0001716155,0.008695306,0.0001764285,0.00007238691,0.0003148863,0.0001856041,0.04203365,0.2069129,0.002928852,0.001203882,0.7368634],"study_design_scores_gemma":[0.00003843398,0.0003970802,0.02298112,0.00002878912,0.00005594841,0.001319882,0.000143742,0.907286,0.06124026,0.003163393,0.003274266,0.00007107162],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09558837,0.0002067844,0.9028189,0.00006628587,0.00002780688,0.00004623075,0.00007428821,0.0004374605,0.0007338412],"genre_scores_gemma":[0.5202157,0.000292073,0.4776188,0.0000386968,0.00004445169,0.00007564754,0.0003273437,0.00005612761,0.001331061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001199822,"threshold_uncertainty_score":0.004272461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2362668783458834,"score_gpt":0.4509924500882153,"score_spread":0.2147255717423319,"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."}}