{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001595948,0.00009974423,0.00009583691,0.00008944111,0.0003557952,0.00002054509,0.0001119096,0.0000270431,0.0006196005],"category_scores_gemma":[0.00001449766,0.0001224636,0.00004705366,0.000384881,0.00001925908,0.00009693998,0.00005283154,0.000308835,0.0000811452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001145056,"about_ca_system_score_gemma":0.000005789861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003140828,"about_ca_topic_score_gemma":0.00000315012,"domain_scores_codex":[0.9993091,0.00002435185,0.0002205934,0.0001724679,0.00009636425,0.0001770737],"domain_scores_gemma":[0.9997075,0.00002889151,0.00004126274,0.0001546362,0.00003178645,0.00003590947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001935527,0.00003327187,0.00003261195,0.00001940026,0.000005255156,0.000004282174,0.0002077499,0.606719,0.05199419,0.0001394641,0.0000252939,0.3408176],"study_design_scores_gemma":[0.000008056579,0.00002722219,0.000002326789,0.00001234769,0.000005447228,0.00004369082,0.0004380051,0.7723105,0.2059621,0.001462169,0.01959942,0.0001287184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1073843,0.0003073815,0.8885829,0.00006570332,0.0002087028,0.0002879702,0.00003048614,0.0007358546,0.002396731],"genre_scores_gemma":[0.9392877,0.0001955042,0.05953992,0.00003108586,0.0001477049,0.000116679,0.00009288258,0.0000529863,0.0005355664],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8319034,"threshold_uncertainty_score":0.6784191,"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."}}