{"id":"W2028124797","doi":"10.1016/j.specom.2012.08.007","title":"Multitaper MFCC and PLP features for speaker verification using i-vectors","year":2012,"lang":"en","type":"article","venue":"Speech Communication","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":false,"ca_institutions":"Computer Research Institute of Montréal; Institut National de la Recherche Scientifique","funders":"National Institute of Standards and Technology","keywords":"Multitaper; Mel-frequency cepstrum; Computer science; NIST; Speech recognition; Pattern recognition (psychology); Cepstrum; Speaker recognition; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004597358,0.0001083353,0.0001031612,0.00007493769,0.0003213752,0.0001706121,0.0005189822,0.00007100602,0.000003573621],"category_scores_gemma":[0.0001086791,0.0001040842,0.00003349165,0.0001929404,0.00005046402,0.001112449,0.0001781457,0.0001097535,0.000009183477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005753657,"about_ca_system_score_gemma":0.0000283453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004873599,"about_ca_topic_score_gemma":0.0000153378,"domain_scores_codex":[0.999227,0.00006168074,0.0001560482,0.0001778505,0.0001349128,0.000242503],"domain_scores_gemma":[0.9988203,0.0001109446,0.0001183012,0.0007661422,0.0001065934,0.00007772683],"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":[0.00003460749,0.0002884244,0.02377885,0.0001141171,0.00005454465,3.674055e-7,0.005978219,0.00007099264,0.2235007,0.01563733,0.002087479,0.7284544],"study_design_scores_gemma":[0.001417461,0.0000827648,0.1169945,0.0001886491,0.00006567688,0.0001346788,0.0004649513,0.02426201,0.8029146,0.007589765,0.04495444,0.0009304811],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5740828,0.007384897,0.4129843,0.002054519,0.0003934902,0.0006151833,0.000004217732,0.0002853486,0.002195247],"genre_scores_gemma":[0.5863629,0.0000678771,0.4132913,0.0001322102,0.00004847722,0.00001142097,0.00001008807,0.000008191956,0.0000675437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7275239,"threshold_uncertainty_score":0.4244431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03390941946555964,"score_gpt":0.2998207171212208,"score_spread":0.2659112976556612,"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."}}