{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006973165,0.0000986647,0.0001479145,0.0002281041,0.00005639718,0.0001909412,0.0003129224,0.0001132087,0.0002707953],"category_scores_gemma":[0.0001287722,0.00009314877,0.00004107806,0.000330589,0.00001483378,0.0005462977,0.00005865229,0.0002071485,0.0007751149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002012761,"about_ca_system_score_gemma":0.00002697314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001003394,"about_ca_topic_score_gemma":0.0002326022,"domain_scores_codex":[0.998943,0.00008175147,0.0002487539,0.0002731352,0.0002225683,0.0002307757],"domain_scores_gemma":[0.9993403,0.0001107022,0.00006555776,0.0002867714,0.0001283524,0.00006829914],"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":[0.00000771638,0.0006418005,0.006338164,0.0000258131,0.0000123678,0.0001275431,0.0002146593,2.775598e-7,0.009024365,0.04712059,0.002594329,0.9338924],"study_design_scores_gemma":[0.01083362,0.0005398064,0.1302722,0.0003652679,0.00003848914,0.001519138,0.001493237,0.4788631,0.2504253,0.06325575,0.05940903,0.00298507],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7980651,0.00002855916,0.1065812,0.0009782542,0.002450165,0.0005172989,0.000008277025,0.0005001059,0.09087107],"genre_scores_gemma":[0.9722929,0.000007635642,0.02641103,0.0003264428,0.0000578667,0.00003320664,0.00001682871,0.000007877981,0.0008462271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9309073,"threshold_uncertainty_score":0.9962792,"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."}}