{"id":"W178303711","doi":"","title":"Towards a noisy-channel model of dysarthria in speech recognition","year":2010,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Dysarthria; Speech recognition; Computer science; Speech processing; Distortion (music); Vocal tract; Psychology","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":[0.000275745,0.00007481567,0.0001120455,0.0001243001,0.00002561574,0.00004638229,0.0003337545,0.00006443712,0.00002889094],"category_scores_gemma":[0.00005213653,0.00006615801,0.00003110943,0.0002746391,0.000023094,0.0005162787,0.0001061221,0.0001495401,0.00003026904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006951007,"about_ca_system_score_gemma":0.00009602943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005345362,"about_ca_topic_score_gemma":0.0002191158,"domain_scores_codex":[0.9992453,0.000008568411,0.0001811075,0.0002084521,0.0001609603,0.0001956099],"domain_scores_gemma":[0.9995734,0.00001359138,0.00005600254,0.0002230595,0.00008547652,0.0000484637],"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":[0.000006456881,0.00008986999,0.0001217318,0.00001825257,0.000002567076,0.000006106238,0.0004489918,0.0001279348,0.2192269,0.0009675755,0.0001758094,0.7788078],"study_design_scores_gemma":[0.000266312,0.00002220162,0.0001666706,0.0000188575,9.1586e-7,0.000008716737,0.00001537998,0.1751445,0.7562523,0.06799114,0.00001456762,0.00009840519],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5718864,0.00001200908,0.4091588,0.0005680784,0.0001939379,0.00006833146,0.000001533544,0.00007843503,0.01803247],"genre_scores_gemma":[0.6656512,0.000003614048,0.3340533,0.0001569405,0.00002326449,0.000003187033,8.576727e-7,0.000003418967,0.0001041656],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7787094,"threshold_uncertainty_score":0.2697845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02937504064681019,"score_gpt":0.2505038462371992,"score_spread":0.221128805590389,"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."}}