{"id":"W3094312606","doi":"10.3390/s20216008","title":"Impact of Feature Selection Algorithm on Speech Emotion Recognition Using Deep Convolutional Neural Network","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":127,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Computer science; Discriminative model; Convolutional neural network; Speech recognition; Random forest; Support vector machine; Feature selection; Artificial intelligence; Context (archaeology); Emotion recognition; Feature (linguistics); Feature extraction; Artificial neural network; Emotion classification; Task (project management); Pattern recognition (psychology); Selection (genetic algorithm)","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":[0.001691137,0.001807061,0.001124591,0.0007290328,0.0004061564,0.0006570704,0.0007999757,0.000653665,0.001510868],"category_scores_gemma":[0.004000268,0.0002132945,0.0006810465,0.0005339832,0.0002270254,0.0009456191,0.0005469582,0.000959831,0.0007172802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005339981,"about_ca_system_score_gemma":0.0009286189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008776374,"about_ca_topic_score_gemma":0.007963705,"domain_scores_codex":[0.9990175,0.0001954605,0.00008986548,0.000259629,0.0002588544,0.0001785618],"domain_scores_gemma":[0.9990461,0.0004306765,0.00005075217,0.00006772737,0.0003638636,0.00004086156],"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":[0.001632138,0.0008116058,0.0111265,0.0002047309,0.0002382737,0.0003689198,0.00009487224,0.07637151,0.03029983,0.0004838154,0.01393284,0.864435],"study_design_scores_gemma":[0.00008145933,0.0002402154,0.004959562,0.00003030247,0.00009378236,0.0001429488,0.00007472064,0.9659285,0.02636056,0.000422566,0.001640592,0.00002482607],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6902574,0.006990463,0.282121,0.0009945896,0.0009423058,0.000373834,0.001251125,0.01150764,0.005561601],"genre_scores_gemma":[0.9133496,0.0007598485,0.07773701,0.0003577816,0.00007474494,0.0002316547,0.003765081,0.0002174344,0.003506964],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.008776374,"threshold_uncertainty_score":0.01745057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05434416167619265,"score_gpt":0.324616740495732,"score_spread":0.2702725788195393,"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."}}