{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003010074,0.00006151058,0.0001266704,0.002050955,0.0001594372,0.00007656184,0.0007638621,0.0001078509,0.0001095077],"category_scores_gemma":[0.0008453814,0.00005970119,0.00002671718,0.00174697,0.0008198629,0.0004543758,0.0002593288,0.0006889208,0.00004360338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002792501,"about_ca_system_score_gemma":0.000223462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001141829,"about_ca_topic_score_gemma":0.00002608932,"domain_scores_codex":[0.9981781,0.0003145327,0.0004377225,0.000200271,0.0006503061,0.0002191067],"domain_scores_gemma":[0.9951349,0.0001912112,0.0002209134,0.000312178,0.00406966,0.00007112896],"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":[0.00006750523,0.0002407522,0.00120366,0.000008259089,0.00001977834,0.0001745819,0.0005111931,0.000005693842,0.02759693,0.04049929,0.00008192324,0.9295905],"study_design_scores_gemma":[0.0144633,0.001460617,0.07482148,0.00479927,0.00003416356,0.02819679,0.2361509,0.001023652,0.2385925,0.3532413,0.04631219,0.0009038771],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9579193,0.001367689,0.0004480635,0.01430026,0.001090106,0.0001280949,0.000004227753,0.00003690848,0.02470537],"genre_scores_gemma":[0.991956,0.001311282,0.006461712,0.0000524845,0.00004480675,0.00001071187,0.000006675987,0.00000568352,0.0001506307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9286866,"threshold_uncertainty_score":0.3020819,"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."}}