{"id":"W4393466957","doi":"10.5281/zenodo.6989809","title":"Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Speech recognition; Wake; Natural language processing; Computer science; Linguistics; Engineering; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0005750215,0.0028615,0.001166364,0.001258391,0.0009063981,0.0009434071,0.001819162,0.002202413,0.02308594],"category_scores_gemma":[0.002931655,0.0004270744,0.001308578,0.001033482,0.0004304978,0.001261481,0.001988987,0.001568888,0.04269863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007382861,"about_ca_system_score_gemma":0.000936418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009634212,"about_ca_topic_score_gemma":0.01645102,"domain_scores_codex":[0.9992365,0.0001289753,0.00012834,0.0002276303,0.0001568925,0.0001218013],"domain_scores_gemma":[0.9991574,0.0001879795,0.00005498371,0.0002063779,0.0002897473,0.0001034127],"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":[0.001398909,0.000316607,0.003619107,0.001729285,0.0001625584,0.0004626428,0.0003176407,0.001185174,0.01256747,0.0005464221,0.9244491,0.05324513],"study_design_scores_gemma":[0.001356108,0.001678135,0.1113574,0.001038272,0.000499253,0.003252327,0.002096358,0.02845914,0.03563244,0.004585634,0.8093633,0.0006816279],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02731577,0.001214932,0.005419141,0.0004182919,0.0007480629,0.0005623842,0.9471244,0.01050996,0.006687088],"genre_scores_gemma":[0.0145912,0.0001522173,0.003589451,0.0001847404,0.00005738456,0.0006837347,0.9771839,0.0003472647,0.00321],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02308594,"threshold_uncertainty_score":0.0772301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08792595272007515,"score_gpt":0.3192981323848124,"score_spread":0.2313721796647372,"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."}}