{"id":"W4319596491","doi":"10.3390/electronics12040839","title":"Speech Emotion Recognition Based on Multiple Acoustic Features and Deep Convolutional Neural Network","year":2023,"lang":"en","type":"article","venue":"Electronics","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":136,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mel-frequency cepstrum; Speech recognition; Computer science; Convolutional neural network; Feature (linguistics); Optimal distinctiveness theory; Pattern recognition (psychology); Artificial intelligence; Jitter; Feature extraction; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003026683,0.0007661241,0.0004576356,0.000530638,0.0001959772,0.0004092121,0.0004491487,0.0003859769,0.001269539],"category_scores_gemma":[0.0005562195,0.0002163022,0.0004309942,0.0003026518,0.0001492654,0.000588808,0.000519946,0.0006680303,0.0006340886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004017758,"about_ca_system_score_gemma":0.0002829212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005469516,"about_ca_topic_score_gemma":0.007925728,"domain_scores_codex":[0.9997787,0.00002535156,0.00001432026,0.00007213183,0.00007083151,0.00003873681],"domain_scores_gemma":[0.9998336,0.00003445473,0.0000160682,0.00001583189,0.00008940125,0.00001067433],"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":[0.0005106234,0.0002023205,0.003337311,0.000120213,0.0001301102,0.0001736186,0.00008134785,0.04135742,0.1174208,0.001234079,0.004586086,0.8308461],"study_design_scores_gemma":[0.000009180528,0.00008336758,0.003860016,0.00001226622,0.00005941716,0.00008608848,0.00002879535,0.9704348,0.02333616,0.0006622578,0.001410317,0.00001741318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1845416,0.003164633,0.8006275,0.0004090905,0.00039619,0.0001096727,0.0005884637,0.003460158,0.00670273],"genre_scores_gemma":[0.8926023,0.001167724,0.09721356,0.0001976747,0.00009933064,0.00007897187,0.001213741,0.00006866634,0.007358116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005469516,"threshold_uncertainty_score":0.01087534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01374466518818066,"score_gpt":0.2307959788309115,"score_spread":0.2170513136427309,"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."}}