{"id":"W1689583599","doi":"10.1109/iscas.2015.7168736","title":"A new audiovisual emotion recognition system using entropy-estimation-based multimodal information fusion","year":2015,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Emotion recognition; Entropy (arrow of time); Pattern recognition (psychology); Fusion; Speech recognition; Image fusion; Feature extraction; Support vector machine; Facial expression; Kernel (algebra); Component (thermodynamics); Machine learning; Image (mathematics); Mathematics","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":[0.0007792944,0.0008292592,0.001075198,0.001128233,0.0003251168,0.0008759592,0.0009684777,0.0006571724,0.002523053],"category_scores_gemma":[0.001066134,0.0002660783,0.0007269283,0.000647133,0.0002672669,0.001388601,0.001115906,0.0006117684,0.001205022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003280312,"about_ca_system_score_gemma":0.0002659669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008041755,"about_ca_topic_score_gemma":0.0006480492,"domain_scores_codex":[0.9994169,0.00007560255,0.00004663214,0.0001729049,0.0002286447,0.00005936524],"domain_scores_gemma":[0.9996589,0.00005374712,0.00003561165,0.00003537134,0.0001845611,0.00003167436],"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":[0.0007435759,0.0001903419,0.001568344,0.0002243086,0.0001677651,0.0002270109,0.0002060876,0.008330134,0.2994093,0.002409971,0.004620226,0.6819031],"study_design_scores_gemma":[0.0001069338,0.0005489589,0.006307091,0.00005258175,0.0003018179,0.0006837014,0.0001259527,0.8002589,0.1779762,0.004385174,0.009095628,0.0001569621],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02363115,0.000519693,0.9698785,0.0001335058,0.0001363056,0.0001220275,0.0001732884,0.003391522,0.002013983],"genre_scores_gemma":[0.4632775,0.0005752261,0.5289147,0.0003778744,0.0002301807,0.0003317512,0.0007427977,0.0002085575,0.005341364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002523053,"threshold_uncertainty_score":0.008440495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03436282615057315,"score_gpt":0.2635342234739154,"score_spread":0.2291713973233422,"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."}}