{"id":"W6998712466","doi":"","title":"American/Canadian English Speech Recognition Corpus (headset+mobile)","year":2020,"lang":"en","type":"other","venue":"Americanae (AECID Library)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Speech corpus; Sequence (biology); Background noise; Voice activity detection; Speech processing; Noise (video); Speech technology; Speaker recognition","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001081804,0.002922883,0.00127573,0.00403144,0.003277548,0.001703627,0.002709537,0.001035218,0.09246662],"category_scores_gemma":[0.003354914,0.0004529359,0.0005426971,0.005369216,0.0009004231,0.0008899112,0.001334804,0.001037487,0.06720253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004914662,"about_ca_system_score_gemma":0.01287444,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8080366,"about_ca_topic_score_gemma":0.8461338,"domain_scores_codex":[0.9983814,0.0001656945,0.0001166273,0.0002764264,0.0007574939,0.0003023165],"domain_scores_gemma":[0.9950748,0.0003924203,0.00008719515,0.0003401852,0.00374867,0.0003566527],"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":[0.0004538506,0.0001579637,0.001141881,0.001278483,0.00003660288,0.0004425139,0.0004051197,0.0006446686,0.007352313,0.001380097,0.9220044,0.06470209],"study_design_scores_gemma":[0.0002035468,0.00008544517,0.03626223,0.0002187778,0.0001528516,0.0009361955,0.0008571397,0.003461894,0.00920725,0.00043497,0.9479954,0.0001842299],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0185145,0.001268356,0.004731918,0.0004183275,0.000445887,0.0008161504,0.9197373,0.004598004,0.04946966],"genre_scores_gemma":[0.02684437,0.0007333686,0.005965467,0.000181587,0.00008643412,0.0008690752,0.9286613,0.0007095635,0.03594884],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1919634,"threshold_uncertainty_score":0.3861879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009377824478750968,"score_gpt":0.2074177189699123,"score_spread":0.1980398944911613,"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."}}