{"id":"W4388722993","doi":"10.3390/app132212410","title":"A Feature Selection Algorithm Based on Differential Evolution for English Speech Emotion Recognition","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Advanced Computing and Algorithms","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Jilin Office of Philosophy and Social Science","keywords":"Computer science; Speech recognition; Artificial intelligence; Classifier (UML); Mel-frequency cepstrum; Feature selection; Discrete cosine transform; Pattern recognition (psychology); Population; Prosody; Differential evolution; Feature extraction","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008707511,0.0007003319,0.001057583,0.0008468832,0.0003234319,0.0004011299,0.0008427283,0.0005281445,0.001051696],"category_scores_gemma":[0.002337292,0.0003100318,0.0007717689,0.0007338438,0.0002825047,0.0004233495,0.0004811688,0.0006367177,0.0002128419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005043124,"about_ca_system_score_gemma":0.0005765466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004310306,"about_ca_topic_score_gemma":0.002999074,"domain_scores_codex":[0.9995757,0.00008953952,0.00003866031,0.0001058965,0.000150089,0.00004009082],"domain_scores_gemma":[0.9994541,0.0002552857,0.00003884222,0.00002976109,0.000201849,0.00002020692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001346813,0.0001207837,0.002841834,0.00009950534,0.0001435211,0.000183766,0.0001294827,0.4424116,0.01723783,0.003873523,0.002253255,0.5305703],"study_design_scores_gemma":[0.00001511463,0.0000476632,0.0005525768,0.000003480189,0.00001269195,0.00004352882,0.000007092628,0.9967878,0.001475608,0.0004386556,0.0006098394,0.000006035265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02406876,0.0002325508,0.9740961,0.00008656644,0.00004959297,0.00007464836,0.00004033738,0.0004673864,0.0008840314],"genre_scores_gemma":[0.4309967,0.0002274118,0.5652834,0.0001760452,0.00004807481,0.0004924072,0.0003884711,0.0001102563,0.002277148],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004310306,"threshold_uncertainty_score":0.008570433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02650012965361479,"score_gpt":0.2986559911649881,"score_spread":0.2721558615113733,"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."}}