{"id":"W3200866700","doi":"10.1109/access.2021.3111659","title":"Speech Emotion Recognition Using Clustering Based GA-Optimized Feature Set","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Speech recognition; Cluster analysis; Support vector machine; Feature (linguistics); Artificial intelligence; Feature extraction; Field (mathematics); Speaker recognition; Pattern recognition (psychology); Set (abstract data type); Context (archaeology); Outlier; Word error rate","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002333524,0.0001769949,0.0002096828,0.0001674856,0.0001478654,0.0001723748,0.0001417107,0.0002605628,0.004183497],"category_scores_gemma":[0.00005197942,0.0001919098,0.0001246704,0.000405671,0.00002696726,0.0003579555,0.00003557482,0.0002753767,0.0003021774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000695149,"about_ca_system_score_gemma":0.00005497294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006726837,"about_ca_topic_score_gemma":0.0001165932,"domain_scores_codex":[0.9985384,0.0002986147,0.0002422144,0.0004446132,0.0001862455,0.0002899381],"domain_scores_gemma":[0.9991233,0.00006757802,0.0001494384,0.0002989126,0.000268069,0.00009269758],"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":[0.002133862,0.002417509,0.004209708,0.0008926773,0.0007873507,0.00239982,0.003436779,0.007724465,0.130213,0.00007189928,0.08524044,0.7604725],"study_design_scores_gemma":[0.05529185,0.0006981324,0.06430147,0.003906014,0.001967654,0.004879153,0.006267495,0.2165228,0.6078157,0.008314474,0.02354888,0.006486381],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8419717,0.00008733555,0.1307415,0.0008543488,0.00604522,0.0003702342,0.00009153716,0.0002633646,0.01957473],"genre_scores_gemma":[0.9793463,0.00002663157,0.01386512,0.002869224,0.0009296585,0.00003419804,0.0007588258,0.00006876663,0.002101333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7539861,"threshold_uncertainty_score":0.9967268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1360437984457945,"score_gpt":0.3878721367828022,"score_spread":0.2518283383370077,"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."}}