{"id":"W2914422976","doi":"10.48550/arxiv.1902.02375","title":"Centroid-based deep metric learning for speaker recognition","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Embedding; Computer science; Centroid; Speech recognition; Similarity (geometry); Task (project management); Artificial intelligence; Metric (unit); Set (abstract data type); Speaker recognition; Pattern recognition (psychology); Speaker diarisation; Natural language processing; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037928,0.000308448,0.0003647474,0.0007252021,0.0001714735,0.0001881991,0.001021947,0.0003207772,0.0001913683],"category_scores_gemma":[0.0002700195,0.0003670075,0.0004273469,0.0007996113,0.00004092844,0.0002946854,0.0004434447,0.0004887137,0.0007023268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002450929,"about_ca_system_score_gemma":0.0001859166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003459486,"about_ca_topic_score_gemma":0.00001625695,"domain_scores_codex":[0.9979239,0.0002093126,0.0002165092,0.001118275,0.0001218395,0.0004101974],"domain_scores_gemma":[0.9979144,0.0005550182,0.0003276969,0.0007039006,0.0003384245,0.0001606055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003884725,0.0008057713,0.007619415,0.0009050508,0.0006334352,0.0004002586,0.0003150077,0.6296287,0.0002616434,0.02009472,0.00197806,0.3369694],"study_design_scores_gemma":[0.0009609934,0.00007805022,0.000420649,0.00009541555,0.0001113634,0.000002721452,0.00005344432,0.9812662,0.001283488,0.01142043,0.003760009,0.0005472362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03237444,0.00005022997,0.9615035,0.0001169866,0.0008385443,0.0006106955,0.00002209222,0.0003510173,0.004132465],"genre_scores_gemma":[0.9727255,0.00008218408,0.02500626,0.0002810087,0.0001049902,0.000004449527,0.0001408975,0.00003067542,0.001624081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.940351,"threshold_uncertainty_score":0.9998782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09792118983636798,"score_gpt":0.1929767119206509,"score_spread":0.09505552208428293,"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."}}