{"id":"W4313042211","doi":"10.1109/ijcnn55064.2022.9892054","title":"Fine-grained Early Frequency Attention for Deep Speaker Recognition","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Focus (optics); Deep neural networks; Deep learning; Artificial intelligence; Key (lock); Artificial neural network; Speech recognition; Pattern recognition (psychology)","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":[0.0008560797,0.001311216,0.0005880861,0.0005845047,0.0003615652,0.0006019056,0.001340536,0.000916229,0.005220086],"category_scores_gemma":[0.001549765,0.0003481119,0.000576944,0.0004745597,0.0003647123,0.001699588,0.001537313,0.002074636,0.001919485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007895608,"about_ca_system_score_gemma":0.0007592302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008406718,"about_ca_topic_score_gemma":0.01472137,"domain_scores_codex":[0.9996562,0.00006701447,0.00001282209,0.0001160666,0.00007076295,0.00007701579],"domain_scores_gemma":[0.9996274,0.0001368567,0.00002742763,0.00008497288,0.00008794555,0.00003546743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006445996,0.0003420092,0.002728833,0.0002008845,0.0001738482,0.0001667268,0.0001624104,0.1228644,0.08692648,0.006206466,0.0212495,0.7583339],"study_design_scores_gemma":[0.00003477029,0.0001350575,0.001346886,0.00002244245,0.00004628107,0.00008639675,0.00003507208,0.9474866,0.03644558,0.009200965,0.005136097,0.00002379965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1076034,0.004004246,0.8594617,0.0008937825,0.0004642307,0.0001059238,0.001297962,0.01985872,0.006310064],"genre_scores_gemma":[0.7852023,0.00100982,0.19876,0.0006602957,0.0001732167,0.0001273298,0.003462397,0.0004115044,0.01019305],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008406718,"threshold_uncertainty_score":0.01746291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05429654350573929,"score_gpt":0.2565625299664059,"score_spread":0.2022659864606666,"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."}}