{"id":"W4295919040","doi":"10.1109/ic3sis54991.2022.9885578","title":"Speech Emotion Recognition Using Bagged Support Vector Machines","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Set (abstract data type); Hidden Markov model; Artificial neural network; Estimator; Emotion recognition; Voice activity detection; Mixture model; Kernel (algebra); Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001510224,0.000297482,0.0003293959,0.0004405406,0.0009223358,0.0002233777,0.0007429703,0.0001333528,0.01192655],"category_scores_gemma":[0.00009196649,0.0003390921,0.0001384564,0.0002899982,0.000116738,0.0002008361,0.0004526702,0.0008040528,0.0002613604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000305079,"about_ca_system_score_gemma":0.0000830448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001176387,"about_ca_topic_score_gemma":0.0000839647,"domain_scores_codex":[0.9962553,0.001359307,0.0008610116,0.0005818773,0.0006464065,0.0002960455],"domain_scores_gemma":[0.9978082,0.0002017656,0.0006594978,0.0006130734,0.0005892707,0.0001281788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001375822,0.006335867,0.0101208,0.0003840867,0.001943723,0.00007339694,0.06592646,0.001572407,0.002732927,0.6586612,0.03276979,0.2181035],"study_design_scores_gemma":[0.004437193,0.002054119,0.009970008,0.001119978,0.000329241,0.002153899,0.06561027,0.7629947,0.0009510802,0.02972296,0.1178607,0.002795873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8915218,0.0008413527,0.006483838,0.002550152,0.008580614,0.001370353,0.0004944333,0.0003685342,0.08778899],"genre_scores_gemma":[0.9957327,0.0003877275,0.0002505918,0.0005012992,0.0002818855,0.00008453905,0.001605996,0.00003583937,0.001119396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7614223,"threshold_uncertainty_score":0.9999061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.108398791139649,"score_gpt":0.3543102198666948,"score_spread":0.2459114287270457,"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."}}