{"id":"W3111767470","doi":"10.1016/j.dib.2020.106652","title":"Audio recordings dataset of genuine and replayed speech at both ends of a telecommunication channel","year":2020,"lang":"en","type":"article","venue":"Data in Brief","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Channel (broadcasting); Telecommunications; Computer science; Speech recognition","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005560276,0.001173478,0.001173089,0.001327334,0.0006100758,0.0007391248,0.001112738,0.001251134,0.00760185],"category_scores_gemma":[0.001442213,0.000227424,0.0005776082,0.001259173,0.0004007363,0.0004996272,0.000774438,0.0006506422,0.008372714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004091174,"about_ca_system_score_gemma":0.0007382287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004041101,"about_ca_topic_score_gemma":0.008664932,"domain_scores_codex":[0.9986368,0.0001318979,0.0001596601,0.0003236897,0.0005723659,0.0001756072],"domain_scores_gemma":[0.9982417,0.0002920946,0.0001287329,0.0003695246,0.0007954009,0.0001726253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.007192134,0.003047078,0.02257747,0.006532676,0.0006270794,0.006194535,0.001208909,0.008684522,0.2426158,0.001349611,0.2211374,0.4788328],"study_design_scores_gemma":[0.001026546,0.005099561,0.4383993,0.0006469911,0.0007942773,0.01372762,0.003777184,0.04968606,0.1504679,0.001485803,0.334314,0.0005747361],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.49821,0.002357067,0.03083476,0.0005139819,0.00131604,0.002281985,0.434682,0.009698091,0.02010609],"genre_scores_gemma":[0.3770245,0.001095239,0.02719562,0.0002868499,0.0003550522,0.001856942,0.5770071,0.0003667422,0.01481196],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.00760185,"threshold_uncertainty_score":0.02543074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07590754986961774,"score_gpt":0.2846322564540964,"score_spread":0.2087247065844787,"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."}}