{"id":"W4385489013","doi":"10.1109/icasspw59220.2023.10193304","title":"Investigation Of The Quality Of Pseudo-Labels For The Self-Supervised Speaker Verification Task","year":2023,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Computer science; Overfitting; Cluster analysis; Discriminative model; Artificial intelligence; Task (project management); Speech recognition; Noise (video); Speaker recognition; Speaker verification; Pattern recognition (psychology); Embedding; Quality (philosophy); Machine learning; Artificial neural network","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":[0.005370632,0.001012928,0.0006272449,0.0004502057,0.000533034,0.001045181,0.001277419,0.001359167,0.001367541],"category_scores_gemma":[0.02070097,0.000362917,0.0003961937,0.0002786907,0.001100209,0.002006639,0.001372877,0.00174724,0.000748069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005261523,"about_ca_system_score_gemma":0.0008022525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001281984,"about_ca_topic_score_gemma":0.002294396,"domain_scores_codex":[0.9973506,0.001353922,0.00009160323,0.0005752207,0.0004887651,0.0001399304],"domain_scores_gemma":[0.9882996,0.007520285,0.0007122022,0.00152506,0.001659025,0.0002837455],"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":[0.003222691,0.0006115476,0.01088466,0.0005379569,0.0002832398,0.0002845898,0.000783943,0.380616,0.1471962,0.007121306,0.003909878,0.444548],"study_design_scores_gemma":[0.0000255328,0.0002681029,0.001941725,0.00001846638,0.00002417565,0.0001276994,0.0000776123,0.9527614,0.04222368,0.001914284,0.000587419,0.00002987402],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3448107,0.0004883629,0.6495777,0.0003977043,0.0001316077,0.0001485411,0.0001918798,0.001828986,0.00242446],"genre_scores_gemma":[0.8511216,0.0001214942,0.1460806,0.0001360494,0.00003843009,0.000087204,0.0005072476,0.0002546032,0.001652752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005370632,"threshold_uncertainty_score":0.02840298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1002430465700601,"score_gpt":0.2985688427223572,"score_spread":0.1983257961522971,"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."}}