{"id":"W2810556878","doi":"10.1145/3204949.3208121","title":"A canadian french emotional speech dataset","year":2018,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Limiting; Computer science; License; Speech recognition; Sample (material); Sampling (signal processing); Resolution (logic); Natural language processing; Artificial intelligence; Telecommunications; Engineering","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":[0.00125909,0.002644282,0.0009531425,0.003797864,0.003433779,0.001764544,0.002221238,0.001684151,0.02615798],"category_scores_gemma":[0.004100909,0.0002892251,0.001104691,0.002969546,0.0007093198,0.0008574733,0.001568265,0.001511665,0.01885291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007395974,"about_ca_system_score_gemma":0.01043412,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8012155,"about_ca_topic_score_gemma":0.8612348,"domain_scores_codex":[0.9980695,0.0002889808,0.0000996156,0.0004319017,0.0007193613,0.0003906802],"domain_scores_gemma":[0.9969838,0.0002570189,0.00005956748,0.0002586173,0.002192032,0.0002489469],"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":[0.000560869,0.0001487611,0.0039587,0.000577432,0.0001182081,0.000488457,0.0003703137,0.001090146,0.004436146,0.001599635,0.9178869,0.06876448],"study_design_scores_gemma":[0.0001984621,0.0001458258,0.07219119,0.0003780769,0.0001591528,0.00104877,0.001374636,0.006603341,0.004615233,0.0007832581,0.9122816,0.000220534],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03069176,0.002729581,0.004730128,0.001044508,0.0006458928,0.0007335746,0.9283563,0.004012826,0.02705545],"genre_scores_gemma":[0.02173936,0.0005306746,0.005012479,0.0002993429,0.00009296639,0.0006379291,0.9595298,0.0002207447,0.01193668],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1987845,"threshold_uncertainty_score":0.3999104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04060516691941487,"score_gpt":0.327588769726637,"score_spread":0.2869836028072221,"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."}}