{"id":"W4414580812","doi":"10.2339/politeknik.1729678","title":"Deep Learning-Based Speech Emotion Recognition for IoT Edge Devices: A Comparative Study","year":2025,"lang":"en","type":"article","venue":"Journal of Polytechnic","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Enhanced Data Rates for GSM Evolution; Artificial neural network; Workflow; Cloud computing; Dependency (UML); Emotion recognition; Deep learning; Edge computing; Feature extraction; Emotion classification","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":[0.0009564038,0.0005612645,0.0004664075,0.0004783207,0.0001614811,0.000667007,0.0004106798,0.0005086639,0.001148381],"category_scores_gemma":[0.001859417,0.0001215827,0.0004880019,0.0003730091,0.0001962421,0.0007626555,0.0004703092,0.0004123757,0.0005943151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004915145,"about_ca_system_score_gemma":0.0002318345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004175769,"about_ca_topic_score_gemma":0.003638558,"domain_scores_codex":[0.9996008,0.00009914207,0.00003292462,0.00008145627,0.0001262539,0.00005941021],"domain_scores_gemma":[0.9994604,0.0002587497,0.00002726625,0.00003950324,0.0001828344,0.00003122676],"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":[0.003398548,0.0011293,0.03933001,0.001344698,0.0004907643,0.000667987,0.0006513014,0.07448881,0.02527807,0.001517994,0.009435314,0.8422671],"study_design_scores_gemma":[0.00008449959,0.002059306,0.09790102,0.0001738062,0.0003909353,0.0005064974,0.001323711,0.8696501,0.01838386,0.001217005,0.00822859,0.00008058165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9606419,0.00514533,0.02406939,0.0003951594,0.0002189733,0.0001132874,0.0006999946,0.0003394101,0.008376629],"genre_scores_gemma":[0.9891349,0.001433351,0.006194409,0.00007074867,0.0000398049,0.00004110068,0.001105486,0.00002185757,0.001958212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004175769,"threshold_uncertainty_score":0.008302927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06634208901597038,"score_gpt":0.3784884502560983,"score_spread":0.3121463612401279,"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."}}