{"id":"W2069483556","doi":"10.1109/ais.2010.5547038","title":"Systems combination in large vocabulary continuous speech recognition","year":2010,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Word error rate; Vocabulary; Speech recognition; Confusion; Word (group theory); Field (mathematics); Frame (networking); Reduction (mathematics); Artificial intelligence; Natural language processing; Linguistics; Telecommunications","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":[0.002180482,0.001456579,0.001474879,0.00110268,0.0006293413,0.001482235,0.001151254,0.0008964211,0.003939123],"category_scores_gemma":[0.002812744,0.0006588456,0.001109498,0.0009097522,0.0006071632,0.001804976,0.001691633,0.0009769305,0.003113528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000382109,"about_ca_system_score_gemma":0.0005450698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008507177,"about_ca_topic_score_gemma":0.001353616,"domain_scores_codex":[0.9967396,0.001091173,0.0003128453,0.000659932,0.001021435,0.0001750964],"domain_scores_gemma":[0.9982962,0.0007829545,0.00008674488,0.0002749862,0.0005151511,0.00004397022],"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":[0.000416853,0.0001226839,0.001500763,0.0008235881,0.0004108642,0.0003760943,0.0001994935,0.0394264,0.05670458,0.005317168,0.002906599,0.8917949],"study_design_scores_gemma":[0.0001145518,0.002194005,0.005258607,0.000233759,0.001246035,0.003626283,0.0002600563,0.6266909,0.2676287,0.02558151,0.06689838,0.0002673247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01991277,0.008090479,0.9621837,0.0001651381,0.0002551185,0.0002086839,0.00009555847,0.002578727,0.006509938],"genre_scores_gemma":[0.3994019,0.005121185,0.577952,0.0003310677,0.0005210026,0.0004540285,0.001147997,0.0006396505,0.01443127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003939123,"threshold_uncertainty_score":0.01317769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01547537497538183,"score_gpt":0.2333352502310596,"score_spread":0.2178598752556778,"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."}}