{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001480043,0.0007514263,0.001289757,0.001427656,0.0004214544,0.0007462149,0.0007249892,0.0005240346,0.001565769],"category_scores_gemma":[0.002354364,0.0001780664,0.001260692,0.001215654,0.0002345223,0.0008350672,0.0005660814,0.0008284134,0.0007724097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003138414,"about_ca_system_score_gemma":0.0003975662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008830581,"about_ca_topic_score_gemma":0.0007524782,"domain_scores_codex":[0.998961,0.0002843303,0.0001197172,0.0002362463,0.0002907548,0.0001079051],"domain_scores_gemma":[0.9992653,0.0003359956,0.00005677678,0.00008086393,0.0002313317,0.00002977244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000360419,0.0004455363,0.006033421,0.0001524091,0.0001734893,0.0001879786,0.0001484759,0.02669654,0.03348605,0.001381041,0.003180824,0.9277539],"study_design_scores_gemma":[0.00002648881,0.0001701225,0.008136703,0.00002073722,0.00008374255,0.0001474203,0.0001332217,0.9624161,0.02390405,0.002921811,0.002006665,0.00003300099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05822165,0.0006418034,0.9384928,0.0001830265,0.00006768391,0.0001261167,0.0002719132,0.001227924,0.0007670543],"genre_scores_gemma":[0.7145414,0.0003675802,0.2820835,0.0001098767,0.00008890887,0.0002905931,0.001137265,0.00007358506,0.001307279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001565769,"threshold_uncertainty_score":0.007827282,"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."}}