{"id":"W2963596039","doi":"10.21437/interspeech.2017-556","title":"Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Normalization (sociology); Computer science; Speech recognition; Artificial neural network; Deep neural networks; Acoustic model; Artificial intelligence; Pattern recognition (psychology); Speech processing","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":[0.001138273,0.001226568,0.0006845937,0.0006446986,0.0004461927,0.001004165,0.001686589,0.0007386819,0.003808103],"category_scores_gemma":[0.00326286,0.0005239649,0.00100675,0.001127578,0.0007779148,0.002154673,0.001180456,0.00237363,0.001548539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001082245,"about_ca_system_score_gemma":0.001063598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006270321,"about_ca_topic_score_gemma":0.009085544,"domain_scores_codex":[0.9992433,0.0001715917,0.00005830278,0.0002621823,0.0002036866,0.00006102776],"domain_scores_gemma":[0.9994872,0.0001669391,0.00005046008,0.0001436882,0.0001344984,0.00001709409],"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":[0.0001799522,0.00008373346,0.0008923872,0.0001512436,0.0001516166,0.0001076601,0.0001523791,0.3878192,0.03031012,0.03036155,0.007937748,0.5418524],"study_design_scores_gemma":[0.000005531231,0.00001487424,0.0001732837,0.00001042883,0.00001676968,0.00003323616,0.00001065273,0.9740179,0.0107869,0.01058531,0.004331235,0.00001400001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003217513,0.0003120539,0.9935643,0.00009273037,0.00007244378,0.00002032456,0.000107766,0.001417703,0.001195132],"genre_scores_gemma":[0.3157452,0.001089645,0.6716536,0.0003626677,0.000163982,0.0003026949,0.001011248,0.001149813,0.008521037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006270321,"threshold_uncertainty_score":0.01273942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1248794939113033,"score_gpt":0.3111123926363031,"score_spread":0.1862328987249999,"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."}}