{"id":"W4405845924","doi":"10.1109/wifs61860.2024.10810699","title":"Efficient Audio Deepfake Detection using WavLM with Early Exiting","year":2024,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Computer science; Speech recognition","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":[0.000735949,0.00109503,0.0007391794,0.000797338,0.0003797451,0.0008876701,0.001010727,0.0009371053,0.002034658],"category_scores_gemma":[0.002240004,0.0003438134,0.0005190854,0.0003528447,0.0005666828,0.001419721,0.001932938,0.001638463,0.001170881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004004782,"about_ca_system_score_gemma":0.0007487058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001856872,"about_ca_topic_score_gemma":0.004252949,"domain_scores_codex":[0.9996158,0.00006106147,0.00002057195,0.00008413065,0.0001297279,0.00008860023],"domain_scores_gemma":[0.9993035,0.0002415886,0.00008132898,0.0001430408,0.0001749585,0.00005555678],"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":[0.0008505248,0.0001714687,0.003662191,0.0001281498,0.00006866015,0.0004488179,0.0001704494,0.06570142,0.09545892,0.004876018,0.005262341,0.8232009],"study_design_scores_gemma":[0.00001664871,0.0001046972,0.0010725,0.00002507781,0.00001479322,0.0001883985,0.00005843792,0.9500098,0.04262555,0.00377022,0.002094417,0.00001956481],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1005765,0.0005200282,0.8909023,0.0003069424,0.0001162921,0.00006417368,0.0002071682,0.004698382,0.002608216],"genre_scores_gemma":[0.7397792,0.0001935376,0.2500928,0.0003319706,0.0000478797,0.00007125678,0.0007009183,0.0002357036,0.008546689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002034658,"threshold_uncertainty_score":0.006806612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01717504615658824,"score_gpt":0.2342154354562108,"score_spread":0.2170403892996226,"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."}}