{"id":"W2112582577","doi":"10.1109/tasl.2007.894527","title":"Speaker and Session Variability in GMM-Based Speaker Verification","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Audio Speech and Language Processing","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":227,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"NIST; Computer science; Speaker recognition; Speech recognition; Session (web analytics); Set (abstract data type); Statistic; Pattern recognition (psychology); Artificial intelligence; Speaker diarisation; Factor (programming language); Feature (linguistics); Statistics; Mathematics; Linguistics","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":[0.004501142,0.0005478809,0.0008487874,0.000860945,0.0004222172,0.0006467479,0.0006530439,0.0008323531,0.001260295],"category_scores_gemma":[0.009684813,0.0003827809,0.0006325946,0.0006905982,0.0005319506,0.001319716,0.000940619,0.0007773634,0.0008870452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004924235,"about_ca_system_score_gemma":0.0005930165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003372102,"about_ca_topic_score_gemma":0.005052742,"domain_scores_codex":[0.9967769,0.001748424,0.0001252059,0.0004892467,0.0006760841,0.0001841329],"domain_scores_gemma":[0.9956408,0.003191883,0.0001873573,0.0005092053,0.0004079868,0.00006278576],"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":[0.002274929,0.0001367568,0.01721282,0.000288425,0.0003021708,0.0002754008,0.0006859694,0.1772145,0.07318453,0.01450597,0.004049864,0.7098687],"study_design_scores_gemma":[0.00003011217,0.0002488515,0.01849447,0.00004383827,0.0001463443,0.0005832875,0.0001297681,0.9142366,0.0552182,0.007438916,0.003343967,0.00008575443],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1092454,0.001012503,0.8849971,0.000140194,0.00009500627,0.00007472569,0.0002906504,0.001854645,0.002289675],"genre_scores_gemma":[0.8162435,0.0003650576,0.1804785,0.00007132749,0.0001017503,0.0001106545,0.0004947974,0.0002683136,0.001866066],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004501142,"threshold_uncertainty_score":0.02380466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01295308220622218,"score_gpt":0.2605477096187157,"score_spread":0.2475946274124935,"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."}}