{"id":"W3006824058","doi":"10.1109/asru46091.2019.9003792","title":"Development of Voice Spoofing Detection Systems for 2019 Edition of Automatic Speaker Verification and Countermeasures Challenge","year":2019,"lang":"en","type":"article","venue":"2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Spoofing attack; Computer science; Convolutional neural network; Speaker verification; Speech recognition; Mel-frequency cepstrum; Speaker recognition; Artificial intelligence; Frame (networking); Bottleneck; Pattern recognition (psychology); Classifier (UML); Replay attack; Biometrics; Feature extraction; Artificial neural network; Authentication (law); Computer security","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":[0.00176294,0.0006874605,0.0007922367,0.000633535,0.0003955416,0.0008736469,0.001233522,0.0013792,0.004229151],"category_scores_gemma":[0.002203982,0.0003683732,0.0004123487,0.0002384432,0.0003349073,0.001391596,0.001253552,0.001761081,0.004626061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006641928,"about_ca_system_score_gemma":0.0009565814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001413604,"about_ca_topic_score_gemma":0.001348336,"domain_scores_codex":[0.999121,0.0001176731,0.00006178023,0.000208801,0.000368023,0.0001226494],"domain_scores_gemma":[0.9985031,0.0001789228,0.00008982624,0.000215684,0.0009095457,0.0001029075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007373882,0.0002709755,0.002525778,0.0002147644,0.0001096246,0.0002485833,0.000154412,0.01378961,0.1943493,0.007453563,0.01975349,0.7603925],"study_design_scores_gemma":[0.00009056139,0.001111825,0.004165334,0.00006377773,0.0001044955,0.0005573293,0.00008171621,0.6837923,0.2557632,0.003208858,0.05097361,0.00008704313],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06442361,0.001643499,0.9054545,0.001025534,0.0007530037,0.0005064917,0.0005012384,0.01717519,0.008516817],"genre_scores_gemma":[0.5109946,0.0007131865,0.4649783,0.0005679818,0.0002049811,0.0003269757,0.002991947,0.0004193273,0.01880273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004229151,"threshold_uncertainty_score":0.01414794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08200242944652283,"score_gpt":0.2663433709157091,"score_spread":0.1843409414691863,"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."}}