{"id":"W4385240187","doi":"10.1038/s41598-023-38868-2","title":"Speech emotion classification using attention based network and regularized feature selection","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Support vector machine; Feature selection; Artificial intelligence; Convolutional neural network; Random forest; Emotion classification; Sentence; Speech recognition; Selection (genetic algorithm); Artificial neural network; Feature (linguistics); Machine learning; Natural language processing","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":[],"consensus_categories":[],"category_scores_codex":[0.001965496,0.0001253904,0.0001316106,0.0003411991,0.0005667656,0.0002100611,0.00003799512,0.000202421,0.0002619556],"category_scores_gemma":[0.00006865833,0.0001272329,0.00007538514,0.001485744,0.0001061211,0.0001744347,0.00001863515,0.0001513957,0.0001180308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006354368,"about_ca_system_score_gemma":0.00005044316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001640481,"about_ca_topic_score_gemma":0.00002208448,"domain_scores_codex":[0.9981157,0.0001935681,0.0003252551,0.0007362388,0.0003230031,0.0003062079],"domain_scores_gemma":[0.9989951,0.00002385899,0.0003235393,0.0003571911,0.0002140553,0.00008621247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000118954,0.0002800693,0.06148149,0.0001118162,0.00009710361,0.000265049,0.0006656773,0.0006251246,0.6295905,0.001079586,0.2261061,0.07957854],"study_design_scores_gemma":[0.001333769,0.0001090702,0.8237885,0.0002566309,0.0002012138,0.0014503,0.000825334,0.1147884,0.003950917,0.01981619,0.03282347,0.0006562332],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9762174,0.00002619805,0.006681767,0.0005267956,0.01369783,0.000472613,0.00000193334,0.0003540604,0.002021373],"genre_scores_gemma":[0.9772229,0.000002704237,0.002732418,0.00005575964,0.0003559127,0.00002545352,0.000716484,0.00002595772,0.01886242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.762307,"threshold_uncertainty_score":0.5188407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05441868366940751,"score_gpt":0.3215790173371756,"score_spread":0.2671603336677681,"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."}}