{"id":"W4318148717","doi":"10.1109/tai.2023.3240113","title":"Fine-Grained Early Frequency Attention for Deep Speaker Representation Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Speech recognition; Deep learning; Convolutional neural network; Artificial intelligence; Speaker recognition; Feature learning; Speech processing; Transfer of learning","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000380983,0.0001598736,0.0001528442,0.0003995909,0.0004481005,0.0002183443,0.0003455338,0.00009238617,0.0001544931],"category_scores_gemma":[0.00009186889,0.0001696977,0.0002178127,0.001219048,0.00005334342,0.0004650655,0.000002654652,0.0002020324,0.001415997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004818932,"about_ca_system_score_gemma":0.00002963058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006607993,"about_ca_topic_score_gemma":0.0002075215,"domain_scores_codex":[0.9983594,0.00008360355,0.0004148417,0.000509484,0.0003018399,0.0003308185],"domain_scores_gemma":[0.9988362,0.0004506312,0.00009929799,0.0003253772,0.0001920255,0.00009651895],"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":[0.00003571836,0.0001418537,0.00001828887,0.00001037595,0.00003042702,0.000007401953,0.0007108613,0.01085925,0.03370591,0.00632635,0.00004275535,0.9481108],"study_design_scores_gemma":[0.00009657309,0.0002789597,0.0005101381,0.0000443223,0.00002955306,0.00001004082,0.0004245621,0.5634347,0.39676,0.03780134,0.0002298248,0.0003799958],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03083206,0.000003548522,0.9656093,0.0009615134,0.001109586,0.0003837741,0.000008933852,0.0006569756,0.0004343227],"genre_scores_gemma":[0.9679639,0.00001656432,0.03065406,0.00007074758,0.00009241971,0.000200095,0.000009020876,0.00002171564,0.0009715153],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9477308,"threshold_uncertainty_score":0.9993615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09106019514806173,"score_gpt":0.3225803774889449,"score_spread":0.2315201823408831,"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."}}