{"id":"W4280625908","doi":"10.18280/ria.360211","title":"Speech Emotion Recognition Using Machine Learning Techniques","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Mel-frequency cepstrum; Speech recognition; Artificial intelligence; Support vector machine; Subspace topology; Pattern recognition (psychology); Classifier (UML); Random subspace method; Feature extraction; Random forest; Surprise; Disgust; Machine learning; Psychology; Communication","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0004738342,0.0004164445,0.0005621178,0.0008151159,0.0001726772,0.0008097864,0.0003338162,0.000555949,0.001625957],"category_scores_gemma":[0.001164722,0.0001184593,0.0005477084,0.0006218069,0.0001431304,0.0007402269,0.0003165118,0.0004630201,0.001555931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001730379,"about_ca_system_score_gemma":0.0001505856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004949331,"about_ca_topic_score_gemma":0.0004311524,"domain_scores_codex":[0.9995071,0.0001148733,0.00004560279,0.0001002711,0.0001953766,0.00003678776],"domain_scores_gemma":[0.99964,0.0001416067,0.0000369609,0.00003500145,0.0001381648,0.000008311925],"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":[0.0001013214,0.00006830596,0.001280461,0.0002007159,0.00007471142,0.0001343018,0.00007022772,0.01263068,0.05934557,0.00136723,0.003645708,0.9210808],"study_design_scores_gemma":[0.00002046869,0.0002340514,0.00802015,0.00008974559,0.00008237985,0.0005370479,0.0001871678,0.9063108,0.06402536,0.005103859,0.0153328,0.0000561942],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04391231,0.00441993,0.9434704,0.0004236615,0.0003581843,0.0001112946,0.0003338292,0.002376628,0.004593724],"genre_scores_gemma":[0.5660154,0.004126278,0.420516,0.0003123573,0.0003368828,0.00019232,0.001091499,0.0001041141,0.007305044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001625957,"threshold_uncertainty_score":0.005439341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04196444423690645,"score_gpt":0.2700542475030155,"score_spread":0.2280898032661091,"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."}}