{"id":"W4414537366","doi":"10.47392/irjaeh.2025.0550","title":"Machine Learning Methods for Speech Emotion Recognition","year":2025,"lang":"en","type":"article","venue":"International Research Journal on Advanced Engineering Hub (IRJAEH)","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Semtech (Canada)","funders":"","keywords":"Convolutional neural network; Support vector machine; Robustness (evolution); Feature extraction; Emotion classification; Mel-frequency cepstrum; Random forest; Generalization; Feature (linguistics); Benchmark (surveying)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003224779,0.0001716842,0.0001919828,0.001398335,0.0003052152,0.000452617,0.000891952,0.00008493168,0.0001439107],"category_scores_gemma":[0.006476077,0.0001682059,0.0001663884,0.0006416403,0.00002640282,0.0006246408,0.0001376198,0.001032511,0.0000691258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005203319,"about_ca_system_score_gemma":0.0001153503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005000834,"about_ca_topic_score_gemma":0.000001193144,"domain_scores_codex":[0.9978539,0.0002466303,0.0004032542,0.0003606376,0.0006888323,0.0004467651],"domain_scores_gemma":[0.9966366,0.001777557,0.0001038098,0.000204812,0.00111023,0.0001670279],"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":[0.00007304713,0.00006389247,0.0000147634,0.00001856,0.0000738331,0.00002005822,0.0000314022,0.004985332,0.009246807,0.01058125,0.000353634,0.9745374],"study_design_scores_gemma":[0.001281602,0.0002976939,0.0003245591,0.000559844,0.000007396649,0.000164029,0.00005092597,0.5973336,0.05065658,0.0328809,0.3161588,0.0002840195],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002022898,0.0002043203,0.9852964,0.003414904,0.002064635,0.0002427328,0.000008714398,0.000159966,0.006585466],"genre_scores_gemma":[0.03632998,0.0007882934,0.9577475,0.0002718621,0.0003976577,0.00009359445,0.00002604542,0.00003342811,0.004311604],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9742534,"threshold_uncertainty_score":0.7752936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07098266512585713,"score_gpt":0.4301191633013124,"score_spread":0.3591364981754553,"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."}}