{"id":"W4285813846","doi":"10.1109/iwcmc55113.2022.9824220","title":"A Study Of Voiceprint Recognition Technology Based on Deep Learning","year":2022,"lang":"en","type":"article","venue":"2022 International Wireless Communications and Mobile Computing (IWCMC)","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Realization (probability); Process (computing); Facial recognition system; Artificial intelligence; Feature extraction","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":[0.0003279624,0.00024211,0.0002660788,0.0003538875,0.0002698411,0.0008359131,0.0004583882,0.0006278615,0.001589106],"category_scores_gemma":[0.0008962811,0.0001674862,0.0004030533,0.0003719068,0.000442574,0.001985808,0.0003831513,0.0006677216,0.0002283682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005821732,"about_ca_system_score_gemma":0.0004838546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002109411,"about_ca_topic_score_gemma":0.001214874,"domain_scores_codex":[0.9997894,0.00003622986,0.00001036191,0.0000506857,0.00008111232,0.00003218809],"domain_scores_gemma":[0.9997721,0.000101146,0.00002314396,0.00001600429,0.00007126667,0.00001629336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001707261,0.0001832338,0.01268044,0.00075558,0.0001526886,0.001042384,0.000625412,0.2609577,0.06268606,0.2829975,0.005509655,0.3722387],"study_design_scores_gemma":[0.000005677517,0.00007909066,0.003213595,0.00003454378,0.00003157915,0.000261016,0.00007449342,0.952383,0.008073089,0.02821546,0.007604806,0.00002355351],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1437638,0.01025623,0.799829,0.002965769,0.0003289181,0.00006711447,0.0001043456,0.000410995,0.04227385],"genre_scores_gemma":[0.9303816,0.004700375,0.04938309,0.0002930113,0.0001280339,0.00003679559,0.00007232549,0.0000396859,0.01496522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002109411,"threshold_uncertainty_score":0.005316138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02877269869195719,"score_gpt":0.285629063645942,"score_spread":0.2568563649539848,"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."}}