{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002687566,0.0006724566,0.0004996979,0.0005800215,0.0002215676,0.000535,0.000526237,0.0005115714,0.003749488],"category_scores_gemma":[0.0005667931,0.0001647883,0.0005388788,0.0003562491,0.0001089125,0.0003895762,0.0002996766,0.000487753,0.001422538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004738577,"about_ca_system_score_gemma":0.000330295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005725342,"about_ca_topic_score_gemma":0.005779674,"domain_scores_codex":[0.9997807,0.00001303004,0.00001655416,0.00007076995,0.00006207471,0.00005680238],"domain_scores_gemma":[0.9998272,0.00002493637,0.00001331298,0.00001056211,0.0001149239,0.000009051073],"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":[0.0007041492,0.0003274417,0.007292158,0.0002320623,0.0001504745,0.0004217542,0.00009940712,0.02521396,0.1343285,0.0008406749,0.01284692,0.8175426],"study_design_scores_gemma":[0.00003161336,0.0002653262,0.01630892,0.00005082816,0.0001138237,0.0003190553,0.0001145316,0.8961379,0.07959694,0.000826601,0.006198828,0.00003554995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4235631,0.003446324,0.5363144,0.0007379025,0.001456137,0.0005675269,0.00361188,0.007516893,0.02278571],"genre_scores_gemma":[0.8833383,0.001326606,0.08960949,0.0002741319,0.000175054,0.0003131099,0.004289811,0.0001132772,0.02056029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005725342,"threshold_uncertainty_score":0.01254332,"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."}}