{"id":"W4407901436","doi":"10.1109/ictis62692.2024.10894357","title":"Transformer-Based Multi-Head Attention for Noisy Speech Recognition","year":2024,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Speech recognition; Transformer; Artificial intelligence; Engineering; Electrical engineering; Voltage","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":[0.0009304344,0.001314502,0.001003375,0.0009994607,0.0003648729,0.0007062744,0.001526436,0.0007115195,0.002680328],"category_scores_gemma":[0.001626407,0.0003328875,0.001130343,0.0006664723,0.000387305,0.001031097,0.001473785,0.001109573,0.001807724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006732226,"about_ca_system_score_gemma":0.0009250305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009883271,"about_ca_topic_score_gemma":0.01520955,"domain_scores_codex":[0.9994017,0.0001245945,0.0000308434,0.0001981663,0.000143902,0.0001007178],"domain_scores_gemma":[0.9995046,0.0001828522,0.00003022506,0.00006424008,0.0001853458,0.00003262441],"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":[0.0009431622,0.000280161,0.002930189,0.0001868004,0.0002190227,0.0002631076,0.000179385,0.09666638,0.05535188,0.002973198,0.006798839,0.8332079],"study_design_scores_gemma":[0.0000252582,0.0002474796,0.002253836,0.00001778328,0.0001519049,0.0002680162,0.0000568611,0.9662352,0.02483616,0.002557333,0.003312773,0.00003725246],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06257996,0.002871802,0.9226044,0.000341243,0.0004040606,0.0001376581,0.0004654689,0.005375322,0.005220086],"genre_scores_gemma":[0.867069,0.001341453,0.117085,0.0006762232,0.0003685378,0.0001493222,0.001469502,0.0003170912,0.01152395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009883271,"threshold_uncertainty_score":0.01965153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08409056606303837,"score_gpt":0.3157916734832383,"score_spread":0.2317011074201999,"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."}}