{"id":"W4393475061","doi":"10.5281/zenodo.1478765","title":"A Canadian French Emotional Speech Dataset","year":2018,"lang":"en","type":"dataset","venue":"Figshare","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Speech recognition; Computer science; Psychology; Natural language processing","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00008018946,0.0002919981,0.0002366292,0.0004110881,0.0001924935,0.0004437726,0.002490243,0.000360878,0.7171206],"category_scores_gemma":[0.0008902342,0.0002943813,0.00009465363,0.0003489409,0.000008750078,0.0002762524,0.0004130218,0.0003207838,0.1219147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001960429,"about_ca_system_score_gemma":0.001147852,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03082012,"about_ca_topic_score_gemma":0.2163225,"domain_scores_codex":[0.9981192,0.00006572697,0.0002330807,0.0006542079,0.0004618211,0.0004659861],"domain_scores_gemma":[0.9977192,0.0001226626,0.0001318163,0.001356632,0.0001884449,0.0004813005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[1.636418e-7,0.00001478032,1.258755e-7,0.00004690145,0.00001637522,0.0002050725,0.000001819475,2.055076e-8,3.950483e-8,9.274492e-7,0.9960412,0.003672537],"study_design_scores_gemma":[0.00006489881,0.0000200599,0.00005151904,0.0006765198,0.000006529191,0.0001258818,4.746711e-7,0.00009354133,0.00001709544,0.00005887098,0.9985252,0.000359392],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[5.087795e-8,0.00003260692,0.000003074456,0.0002605231,0.0003454743,0.0001675199,0.9983752,0.0000598732,0.0007557055],"genre_scores_gemma":[1.37068e-7,0.000004079777,0.002119453,0.002121039,0.0005743511,0.00008017488,0.9948676,0.0000111843,0.0002219621],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.595206,"threshold_uncertainty_score":0.9999508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04705013687903825,"score_gpt":0.2665401403992551,"score_spread":0.2194900035202168,"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."}}