{"id":"W4391149368","doi":"10.1109/icacta58201.2023.10392486","title":"Speech Emotion Recognition using Fully Convolutional Network and Augmented RAVDESS Dataset","year":2023,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Mel-frequency cepstrum; Speech recognition; Spectrogram; Utterance; Classifier (UML); Artificial intelligence; Emotion recognition; Feature extraction; 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004173183,0.0001160529,0.0001160114,0.0001418993,0.0001781674,0.00003321109,0.00004589931,0.0001256338,0.005175065],"category_scores_gemma":[0.00002383395,0.0001180732,0.00003058113,0.0003703664,0.00005895241,0.0001409066,0.00004256684,0.000115996,0.001993862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002931863,"about_ca_system_score_gemma":0.00001647871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001027405,"about_ca_topic_score_gemma":0.0000408452,"domain_scores_codex":[0.9988924,0.0001499027,0.0002260659,0.0003080225,0.0001398287,0.0002838252],"domain_scores_gemma":[0.9995635,0.00006308467,0.00007560675,0.0001373591,0.00007536363,0.00008513412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002996977,0.000288235,0.003423787,0.00006438064,0.0002430158,0.00008486245,0.000402416,0.000115772,0.002698607,0.003287084,0.745213,0.2438792],"study_design_scores_gemma":[0.02375619,0.00130863,0.5736889,0.0009000067,0.0009309837,0.002814617,0.01115906,0.07128061,0.001736843,0.06363793,0.2450488,0.003737375],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9603709,0.00007366257,0.01648233,0.0007397611,0.002915195,0.0005599642,0.00155824,0.0005225867,0.01677738],"genre_scores_gemma":[0.8888345,0.0002088193,0.01087629,0.003254577,0.002374806,0.00007659693,0.08587823,0.00009732632,0.008398852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5702651,"threshold_uncertainty_score":0.9987832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1142147449422356,"score_gpt":0.3542667348902624,"score_spread":0.2400519899480267,"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."}}