{"id":"W3174927464","doi":"","title":"Speech based Emotion Recognition using CNN Classifier","year":2021,"lang":"en","type":"article","venue":"International journal of advance research, ideas and innovations in technology","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sadness; Mel-frequency cepstrum; Computer science; Speech recognition; Boredom; Convolutional neural network; Happiness; Emotion classification; Surprise; Anger; Affective computing; Classifier (UML); Artificial intelligence; Feature extraction; Psychology; Communication","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":[0.001047739,0.00009572098,0.0001784562,0.002644509,0.00009015828,0.00004922851,0.0002149157,0.000217416,0.0005648599],"category_scores_gemma":[0.001504908,0.00009956783,0.00004647115,0.001750461,0.0002414357,0.0003354719,0.00007179821,0.000839384,0.00002483797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002261406,"about_ca_system_score_gemma":0.0002046857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001658874,"about_ca_topic_score_gemma":0.00003415409,"domain_scores_codex":[0.9982303,0.0001876417,0.000648573,0.0002283963,0.0004614156,0.0002437052],"domain_scores_gemma":[0.994159,0.0001503851,0.0002856649,0.0001510889,0.005211661,0.00004223112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002013329,0.0007331482,0.009630613,0.00001780138,0.0001244506,0.000753993,0.0001524592,0.00006981215,0.02801226,0.05922668,0.0008988992,0.9001786],"study_design_scores_gemma":[0.008992882,0.0008538439,0.04850777,0.001587149,0.00004219645,0.006139833,0.01108591,0.00306565,0.04316184,0.8177873,0.05813926,0.0006363322],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9540703,0.0005951044,0.02719138,0.01109889,0.001506983,0.0001320239,0.00001873199,0.00002851887,0.005358038],"genre_scores_gemma":[0.9697234,0.0003709365,0.02895819,0.0003875575,0.0002421095,0.00001096491,0.00003401432,0.00001699012,0.0002558512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8995422,"threshold_uncertainty_score":0.618482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.121743198593281,"score_gpt":0.4535744315797601,"score_spread":0.3318312329864791,"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."}}