{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015618,0.001107425,0.0009755591,0.001303061,0.0002742397,0.001162544,0.001309668,0.0009815613,0.004134641],"category_scores_gemma":[0.003536249,0.0003588926,0.001202377,0.001463181,0.0003573377,0.001132246,0.0006778047,0.001878071,0.002634431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006173407,"about_ca_system_score_gemma":0.0005837807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002532648,"about_ca_topic_score_gemma":0.002141295,"domain_scores_codex":[0.9990864,0.0002345034,0.0001056112,0.0002177598,0.0003087612,0.00004701523],"domain_scores_gemma":[0.99891,0.0005902987,0.00009258373,0.000125051,0.0002674685,0.00001477454],"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":[0.00006660455,0.00008864982,0.0009510252,0.0004597175,0.0001621563,0.0000797352,0.000060806,0.07555005,0.005679538,0.01365674,0.01126495,0.8919801],"study_design_scores_gemma":[0.00001297476,0.00004586783,0.0009467322,0.00007244667,0.00002559184,0.00007479999,0.00003031893,0.9641349,0.00302396,0.02017774,0.01142578,0.00002881872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002732426,0.006576652,0.985512,0.0005277017,0.0003500565,0.0001044008,0.0003821317,0.001411326,0.002403213],"genre_scores_gemma":[0.2150231,0.01507187,0.7518299,0.0006995905,0.001296316,0.0007769844,0.002901144,0.0003261288,0.01207484],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004134641,"threshold_uncertainty_score":0.01383179,"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."}}