{"id":"W4416250889","doi":"10.1109/ijcnn64981.2025.11228198","title":"A Hybrid Neural Approach to Speaker Verification with an Improved Additive Angular Margin Loss","year":2025,"lang":"","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Softmax function; Pattern recognition (psychology); Artificial neural network; Feature extraction; Margin (machine learning); Feature (linguistics); Noise (video); Speaker recognition; Context (archaeology)","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":[0.001294849,0.001021743,0.0007355024,0.0006737817,0.0003171999,0.000668623,0.001779445,0.0009836133,0.002465497],"category_scores_gemma":[0.001910584,0.0003837645,0.000719389,0.0004443891,0.0005315684,0.001883142,0.002010374,0.001360615,0.001248397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005909568,"about_ca_system_score_gemma":0.0006456416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002279878,"about_ca_topic_score_gemma":0.00368508,"domain_scores_codex":[0.9991677,0.0001922809,0.00004497308,0.0002597421,0.0002547797,0.00008050526],"domain_scores_gemma":[0.9994465,0.0001537864,0.00005847079,0.0001127869,0.0002013778,0.00002709458],"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":[0.0004358495,0.0001896649,0.001153834,0.0001177769,0.0001452763,0.0001303927,0.0001176024,0.1907663,0.06932091,0.008818098,0.003016448,0.7257878],"study_design_scores_gemma":[0.000006152082,0.00005991581,0.0002924128,0.000004816067,0.00001678696,0.00005190743,0.000008232003,0.9847109,0.01199078,0.002074507,0.0007743647,0.000009261724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.011595,0.0002752857,0.9860512,0.00008550635,0.00003834481,0.00002888713,0.00004048957,0.0009691546,0.0009161353],"genre_scores_gemma":[0.5821012,0.0003298924,0.4054547,0.0003204021,0.0001314602,0.0001450224,0.0004491209,0.0002344912,0.01083382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002465497,"threshold_uncertainty_score":0.008247852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01596851000656309,"score_gpt":0.2379786563333433,"score_spread":0.2220101463267802,"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."}}