{"id":"W4393818778","doi":"10.5281/zenodo.1219620","title":"A Canadian French Emotional Speech Dataset","year":2018,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Natural language processing; Speech recognition; Psychology; Computer science; Linguistics; Philosophy","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":[0.001232514,0.004674342,0.001586985,0.004484364,0.003366641,0.001993655,0.003641195,0.003026974,0.03030419],"category_scores_gemma":[0.003874104,0.0004560109,0.001551473,0.003698658,0.0008842524,0.001015131,0.001975894,0.002082238,0.03343042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006047118,"about_ca_system_score_gemma":0.007859912,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6019914,"about_ca_topic_score_gemma":0.7240865,"domain_scores_codex":[0.9978979,0.0003075184,0.0001125094,0.0004820536,0.0007705201,0.0004294637],"domain_scores_gemma":[0.9977816,0.0002614114,0.0000583936,0.0003000614,0.001377341,0.0002211691],"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":[0.0003703625,0.0001577911,0.001787146,0.0006163227,0.0001065168,0.0003695542,0.0001473101,0.0009188428,0.001992513,0.0007394493,0.9623829,0.0304114],"study_design_scores_gemma":[0.0002882966,0.0001436977,0.03532222,0.0004341544,0.0001747717,0.001156011,0.0007890476,0.006575541,0.004224601,0.0006714472,0.9500162,0.0002040258],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01071633,0.001419982,0.001376947,0.0004709228,0.0003445923,0.0003919824,0.9730554,0.002555785,0.009668009],"genre_scores_gemma":[0.00564341,0.0002312015,0.001516524,0.0001168137,0.00004143083,0.0003007256,0.987335,0.0001225315,0.004692428],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3980086,"threshold_uncertainty_score":0.8007052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03942008778546861,"score_gpt":0.250193715978695,"score_spread":0.2107736281932264,"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."}}