{"id":"W4416402999","doi":"10.1109/icait68809.2025.11236785","title":"Speech Emotion Recognition System using DenseNet – 121 on MFCC Features","year":2025,"lang":"","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mel-frequency cepstrum; Emotion recognition; Set (abstract data type); Benchmark (surveying); Cepstrum; Training set; Range (aeronautics)","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":[0.0005160885,0.001215038,0.0006535592,0.0007405402,0.0003761018,0.0006031357,0.0007174519,0.0006712382,0.005403511],"category_scores_gemma":[0.0009450223,0.0003043613,0.0005488879,0.0002669007,0.0001751484,0.0009402901,0.0006200635,0.000832351,0.003318978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000661397,"about_ca_system_score_gemma":0.0004897551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006094494,"about_ca_topic_score_gemma":0.006470601,"domain_scores_codex":[0.9996943,0.00003769726,0.00003030244,0.0001122829,0.00008005115,0.00004532333],"domain_scores_gemma":[0.9997527,0.00004571189,0.00001451425,0.00002445848,0.0001469236,0.00001569715],"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":[0.001222528,0.0006487693,0.003243783,0.0004596582,0.0002132826,0.0006010157,0.0002210214,0.0362974,0.1834301,0.002355871,0.03745078,0.7338558],"study_design_scores_gemma":[0.0001060295,0.0005880495,0.008557286,0.00005717959,0.0001550489,0.0003204921,0.0001561674,0.9103518,0.06347724,0.002392886,0.01376488,0.00007298255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3426831,0.002426294,0.5724635,0.0009628469,0.001857291,0.001313182,0.01061625,0.04011321,0.02756427],"genre_scores_gemma":[0.7409304,0.0007221185,0.2062048,0.0005632936,0.0002784071,0.0009792697,0.03048415,0.0004166098,0.01942091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006094494,"threshold_uncertainty_score":0.0180766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05361385917632833,"score_gpt":0.3373004410860649,"score_spread":0.2836865819097366,"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."}}