{"id":"W4382053133","doi":"10.1109/iwbf57495.2023.10157564","title":"On the Use of Cross-module Attention Statistics Pooling for Speaker Verification","year":2023,"lang":"en","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":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Computer science; Pooling; Artificial neural network; Speech recognition; Artificial intelligence; Time delay neural network; Feature extraction; Pattern recognition (psychology); Convolutional neural network; Speaker recognition","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002312519,0.00004108607,0.00004831795,0.00005373405,0.00008085307,0.0001307332,0.0001551871,0.0000199016,0.00006062783],"category_scores_gemma":[0.0003801383,0.0000283592,0.00003481953,0.0001941682,0.00002225882,0.0001680168,0.00002276571,0.00002256691,0.0001798439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009028108,"about_ca_system_score_gemma":0.00001070978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001060763,"about_ca_topic_score_gemma":0.000004600424,"domain_scores_codex":[0.9995003,0.0000240171,0.0001293919,0.0001271025,0.0001306629,0.00008853743],"domain_scores_gemma":[0.9989024,0.0006598802,0.00005363798,0.0002321607,0.0001352112,0.00001669353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001279511,0.00004744189,0.0004933302,0.0000159622,0.00001573547,6.996529e-7,0.00008783449,0.0002457328,0.003365424,0.8480014,0.02036143,0.1273523],"study_design_scores_gemma":[0.0002101009,0.00005411051,0.05406673,0.00001886901,0.000006339807,0.00000121116,0.00002727953,0.8855183,0.01941584,0.0349715,0.005600438,0.0001092972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1029948,5.138454e-7,0.8955563,0.0005982727,0.0001588544,0.0001499176,0.00003036563,0.00008229509,0.0004286863],"genre_scores_gemma":[0.6936855,0.00001091344,0.3011687,0.0005960871,0.00003313465,0.00004802645,0.00004136861,0.0000110866,0.004405195],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8852726,"threshold_uncertainty_score":0.2311589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1666763134449241,"score_gpt":0.3312485794147502,"score_spread":0.1645722659698261,"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."}}