{"id":"W4318570632","doi":"10.1016/j.procs.2023.01.163","title":"Speech Emotion Classification using Ensemble Models with MFCC","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Mel-frequency cepstrum; Disgust; Speech recognition; Emotion classification; Artificial intelligence; Convolutional neural network; Boosting (machine learning); Emotion recognition; Pattern recognition (psychology); Feature extraction; Anger; 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":[],"consensus_categories":[],"category_scores_codex":[0.0004583345,0.00009581415,0.00008334913,0.0003163538,0.0002258751,0.00009466714,0.0002194926,0.00004905838,0.00002718578],"category_scores_gemma":[0.000009378863,0.00008294807,0.00002062979,0.001537961,0.0001623744,0.0004950803,0.00005433406,0.00009259389,0.0004182262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004876869,"about_ca_system_score_gemma":0.0001091292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001186129,"about_ca_topic_score_gemma":0.000003952775,"domain_scores_codex":[0.9987171,0.0000272233,0.0001436315,0.0004660388,0.000332085,0.000313879],"domain_scores_gemma":[0.9993473,0.00002291802,0.00008139974,0.0002164363,0.0002379179,0.00009400935],"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":[0.00008486654,0.0004409839,0.003077761,0.0001065161,0.00003545068,0.00005377462,0.01479539,0.009373778,0.05858453,0.05089845,0.00394521,0.8586033],"study_design_scores_gemma":[0.0003725998,0.0001167782,0.02050189,0.00004647733,0.000009237883,0.0001252138,0.0002226933,0.9728438,0.001711417,0.003745632,0.0001210174,0.0001832922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4859573,0.000004466933,0.5077173,0.0002010808,0.0007776359,0.0001731915,9.640802e-7,0.0002480368,0.004920076],"genre_scores_gemma":[0.9683886,0.000004372377,0.03087507,0.0002083624,0.0002097404,0.00001411105,0.000008122577,0.00001155338,0.0002801362],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.96347,"threshold_uncertainty_score":0.5375592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1261331757574627,"score_gpt":0.3391560618176429,"score_spread":0.2130228860601802,"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."}}