{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004790166,0.00007376153,0.0001958174,0.000069727,0.00002598863,0.00002140428,0.001295667,0.00003798521,0.00006041167],"category_scores_gemma":[0.0003768262,0.00007525107,0.0000134009,0.000324799,0.0000502833,0.0004320799,0.001459089,0.00007401211,0.000008848897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001185698,"about_ca_system_score_gemma":0.00002051836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002734524,"about_ca_topic_score_gemma":0.0001029991,"domain_scores_codex":[0.999022,0.00006894345,0.0003277878,0.0003329829,0.0001440692,0.0001042353],"domain_scores_gemma":[0.9982113,0.0001401534,0.0001837637,0.001378439,0.00002806415,0.00005826715],"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":[0.0001892127,0.0003067929,0.004266253,0.0002612935,0.00008072038,0.00004193192,0.001893734,0.000002753977,0.03066147,0.0006988294,0.2460004,0.7155966],"study_design_scores_gemma":[0.003583988,0.0006286836,0.0399211,0.0003445019,0.00006986675,0.000222269,0.0002651702,0.2205707,0.2030649,0.0009811713,0.5293938,0.0009537943],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8223039,0.001776026,0.08600511,0.02645185,0.0004326351,0.001431278,0.05665722,0.0003018782,0.004640133],"genre_scores_gemma":[0.7148703,0.001383079,0.267114,0.002520664,0.00007018504,0.00001334122,0.01395949,0.00002204056,0.00004696262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7146428,"threshold_uncertainty_score":0.3068649,"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."}}