{"id":"W4402587421","doi":"10.2139/ssrn.4960965","title":"Enhanced Multi-Modal Emotion Recognition Using Feature Interaction","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Modal; Feature (linguistics); Emotion recognition; Computer science; Pattern recognition (psychology); Speech recognition; Artificial intelligence; Psychology; Chemistry; Linguistics","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":[0.0002954697,0.0005799856,0.0005793593,0.0003568483,0.0001674476,0.0007383811,0.0003980962,0.0006010497,0.007116831],"category_scores_gemma":[0.0009917746,0.0001579746,0.0005068523,0.0004084765,0.0001124699,0.0008139155,0.001069059,0.0005436347,0.001871374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009967032,"about_ca_system_score_gemma":0.0001117888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002237604,"about_ca_topic_score_gemma":0.0004936989,"domain_scores_codex":[0.9997595,0.00004147997,0.000008741808,0.00006828594,0.00007404159,0.0000480578],"domain_scores_gemma":[0.9996992,0.0001329712,0.00002531297,0.00005062509,0.00006629434,0.00002570409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008897298,0.0001912444,0.001015257,0.0001439174,0.00006853751,0.0001170415,0.00006745035,0.001938994,0.5700039,0.000977608,0.004870628,0.4197157],"study_design_scores_gemma":[0.0001042901,0.0008081138,0.03781083,0.00005333846,0.0002360448,0.001268653,0.0001359734,0.5813761,0.3579112,0.008700782,0.01146588,0.0001287394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2130032,0.0007662026,0.7714015,0.0003087756,0.0004331259,0.0001222471,0.001157983,0.004301298,0.008505751],"genre_scores_gemma":[0.7490588,0.0003316582,0.2415359,0.0003075983,0.0002311342,0.000226477,0.001343551,0.0003757896,0.006589065],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007116831,"threshold_uncertainty_score":0.02380812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04748185065488249,"score_gpt":0.3578741694472442,"score_spread":0.3103923187923617,"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."}}