{"id":"W4285815884","doi":"10.1109/icacite53722.2022.9823781","title":"A Novel Approach to Analyse Speech Emotion using CNN and Multilayer Perceptron","year":2022,"lang":"en","type":"article","venue":"2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Speech recognition; Classifier (UML); Multilayer perceptron; Emotion classification; Artificial intelligence; Emotion recognition; Speech corpus; Perceptron; Affective computing; Natural language processing; Speech processing; Speech synthesis; Artificial neural network","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":[0.000314646,0.0002017468,0.0002118788,0.0009207751,0.0001349903,0.00004399578,0.0002456183,0.00008102205,0.0001022657],"category_scores_gemma":[0.0002109251,0.0002197023,0.00002343397,0.0009636026,0.00007355177,0.0001120435,0.0003750887,0.0006578304,0.000003515942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002022514,"about_ca_system_score_gemma":0.00001395842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002108523,"about_ca_topic_score_gemma":0.000001374849,"domain_scores_codex":[0.998668,0.00003443878,0.0003186612,0.0005165528,0.0002177548,0.000244559],"domain_scores_gemma":[0.9994606,0.00005472704,0.0001194443,0.0001676385,0.0001703294,0.00002724352],"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":[0.0002707713,0.001162136,0.01038802,0.00008565961,0.000230479,0.00002504872,0.006400172,0.08379794,0.1869128,0.2840727,0.0002410066,0.4264133],"study_design_scores_gemma":[0.001865868,0.0003993054,0.024065,0.0002225231,0.00001163603,0.0001958506,0.02986315,0.9384032,0.001370705,0.0009764381,0.001876138,0.0007501629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7890515,0.00005320165,0.2056492,0.0004131748,0.0005208776,0.0002421489,0.0000273917,0.0002752045,0.003767303],"genre_scores_gemma":[0.9643939,0.00001995968,0.03518634,0.0001322167,0.00003216162,0.00004001982,0.0000359468,0.00001863216,0.0001408886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8546053,"threshold_uncertainty_score":0.8959199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05627916348448164,"score_gpt":0.3356722749498035,"score_spread":0.2793931114653218,"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."}}