{"id":"W4353100307","doi":"10.18280/ts.400126","title":"Multi-Attribute Feature Extraction and Selection for Emotion Recognition from Speech through Machine Learning","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Speech recognition; Computer science; Feature selection; Emotion recognition; Selection (genetic algorithm); Artificial intelligence; Feature extraction; Pattern recognition (psychology); Natural language processing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003818655,0.0001807332,0.0001636093,0.0001556216,0.0003024029,0.00005691244,0.00004051489,0.000212106,0.001507847],"category_scores_gemma":[0.00003994432,0.0001873763,0.00008641364,0.0002773995,0.00002436905,0.0002739939,0.00001237654,0.0002857028,0.0003042259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000568817,"about_ca_system_score_gemma":0.000009568586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002066353,"about_ca_topic_score_gemma":0.0001539764,"domain_scores_codex":[0.998706,0.0001776736,0.0002475739,0.0004286039,0.0001635622,0.0002765443],"domain_scores_gemma":[0.9994598,0.0001453283,0.0001569435,0.00005300239,0.0001241086,0.0000607896],"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":[0.001003887,0.001005402,0.01956928,0.000142791,0.0005366903,0.00002100452,0.006294473,0.0002209047,0.267371,0.0001932627,0.02637507,0.6772662],"study_design_scores_gemma":[0.02627703,0.003079628,0.7269895,0.000416698,0.0009074416,0.000257591,0.007137871,0.08532624,0.04996387,0.005224969,0.09255888,0.0018603],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.848956,0.0001218052,0.1467169,0.001103123,0.0008772509,0.0009513471,0.0003094468,0.0005849138,0.0003792469],"genre_scores_gemma":[0.9813614,0.0001500437,0.008500694,0.0002645759,0.0007628657,0.0001998925,0.00583292,0.00004782945,0.002879767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7074202,"threshold_uncertainty_score":0.9994049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07947997511552186,"score_gpt":0.3388430144877355,"score_spread":0.2593630393722137,"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."}}