{"id":"W4254275118","doi":"10.48175/ijarsct-v4-i3-024","title":"Speech Emotion Recognition System","year":2021,"lang":"en","type":"article","venue":"International Journal of Advanced Research in Science Communication and Technology","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Speech recognition; Mel-frequency cepstrum; Disgust; Sadness; Computer science; Surprise; Anger; Set (abstract data type); Emotion classification; Convolutional neural network; Support vector machine; Human voice; Cepstrum; Multilayer perceptron; Artificial intelligence; Feature extraction; Artificial neural network; 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":[0.0005054881,0.001098682,0.001074739,0.000679204,0.0004940079,0.001308084,0.001081061,0.0009666299,0.04300272],"category_scores_gemma":[0.001185118,0.0002406353,0.0006114193,0.0003791947,0.0001344683,0.001018122,0.0009576839,0.0007203433,0.05924249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004577861,"about_ca_system_score_gemma":0.0005782121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001893644,"about_ca_topic_score_gemma":0.001288651,"domain_scores_codex":[0.9993843,0.00005367192,0.00007202578,0.0002212322,0.0002016835,0.0000670649],"domain_scores_gemma":[0.9995674,0.00004385989,0.00001946662,0.00006087833,0.0002856183,0.00002272669],"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":[0.001347625,0.0002725976,0.003017085,0.000906621,0.0001680907,0.0005899507,0.000245899,0.004982736,0.1146799,0.003279018,0.2904133,0.5800973],"study_design_scores_gemma":[0.0003134848,0.0007014333,0.01138311,0.0002194282,0.0003588339,0.002114109,0.000416647,0.2052874,0.2114363,0.005700701,0.5618157,0.0002527641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06263587,0.003417812,0.4817012,0.001604766,0.003115003,0.002350648,0.06883707,0.255946,0.1203917],"genre_scores_gemma":[0.2980921,0.002236847,0.2990696,0.003192321,0.0006775787,0.002877432,0.210928,0.005558652,0.1773675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04300272,"threshold_uncertainty_score":0.1438584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.116474352554005,"score_gpt":0.468626629532395,"score_spread":0.35215227697839,"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."}}