{"id":"W4297415838","doi":"10.1016/j.procs.2022.09.345","title":"Multiple Models Fusion for Multi-label Classification in Speech Emotion Recognition Systems","year":2022,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Utterance; Perspective (graphical); Speech recognition; Field (mathematics); Emotion classification; Emotion recognition; Process (computing); Artificial intelligence; Natural language processing; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003732032,0.001741658,0.001566132,0.001108989,0.0008149776,0.001810551,0.001620335,0.001778353,0.002579669],"category_scores_gemma":[0.004185108,0.0005311017,0.001874934,0.0006221781,0.000483521,0.001933486,0.00204283,0.003055575,0.002563499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008830795,"about_ca_system_score_gemma":0.0006206618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003072883,"about_ca_topic_score_gemma":0.003338371,"domain_scores_codex":[0.9978002,0.0007645741,0.0001475942,0.000606772,0.0003982616,0.00028266],"domain_scores_gemma":[0.9982262,0.0007658385,0.0001136033,0.0002366376,0.0005736976,0.00008402828],"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":[0.001283876,0.0007730632,0.003704424,0.0002631437,0.0005233052,0.0003246901,0.0005100945,0.1508374,0.03696159,0.002621277,0.007004451,0.7951927],"study_design_scores_gemma":[0.00001185206,0.0001334367,0.0008573011,0.00001849155,0.00006181994,0.0000410397,0.00007833628,0.9881448,0.006985094,0.002571551,0.001073914,0.00002237414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08518324,0.003608095,0.9001818,0.001046567,0.0005948456,0.0001979891,0.0003472028,0.005164026,0.003676191],"genre_scores_gemma":[0.8145654,0.0006800637,0.1757098,0.0005449955,0.0003294704,0.0002163064,0.001038025,0.0002860294,0.006629882],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003732032,"threshold_uncertainty_score":0.01973706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1966625564734908,"score_gpt":0.3464360846882111,"score_spread":0.1497735282147203,"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."}}