{"id":"W4417438833","doi":"10.1109/access.2025.3644961","title":"Speech Emotion Recognition Using Cepstral Features Extracted With Gammatone Filter Banks Realized Based on ERB and Mel Frequency Scales","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"dPoint Technologies (Canada)","funders":"Ministry of Electronics and Information technology","keywords":"Mel-frequency cepstrum; Cepstrum; Filter bank; Filter (signal processing); Feature extraction; Pattern recognition (psychology); Feature (linguistics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004292377,0.0006630262,0.0004084771,0.0006921401,0.0001537501,0.0005081022,0.0003216987,0.0004960187,0.001431374],"category_scores_gemma":[0.001552366,0.0001511728,0.0004295598,0.0005529923,0.0001810186,0.0007202022,0.0002668254,0.0005094575,0.001052988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001596232,"about_ca_system_score_gemma":0.0001823098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001062958,"about_ca_topic_score_gemma":0.001315526,"domain_scores_codex":[0.999757,0.00003666749,0.0000161662,0.00006179474,0.0001040455,0.0000242397],"domain_scores_gemma":[0.9996808,0.0001037594,0.00003293671,0.00003682494,0.0001340354,0.00001165952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005572627,0.000105328,0.002302538,0.0001782965,0.0000888213,0.0003295441,0.0001516638,0.01468098,0.2644302,0.002033342,0.003174926,0.7119671],"study_design_scores_gemma":[0.00007672332,0.0006284988,0.03084613,0.0001054344,0.0002574385,0.001449647,0.0002248204,0.72398,0.2261374,0.002017272,0.01414922,0.0001273148],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.121495,0.001301301,0.8715948,0.0001571792,0.0002295383,0.000111168,0.0003238576,0.001665469,0.003121667],"genre_scores_gemma":[0.585198,0.001482299,0.407674,0.0002119807,0.0001118052,0.0001605138,0.001052909,0.0001259611,0.003982489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001431374,"threshold_uncertainty_score":0.004788458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05623014370248006,"score_gpt":0.3605247484347724,"score_spread":0.3042946047322923,"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."}}