{"id":"W1997805633","doi":"10.1145/1640377.1640388","title":"Avoiding speaker variability in pronunciation verification of children's disordered speech","year":2009,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shanghai Ocean University; Universität Wien; McGill University","keywords":"Normalization (sociology); Pronunciation; Speech recognition; Computer science; Word error rate; Speaker recognition; Adaptation (eye); Artificial intelligence; Speaker verification; Test set; Pattern recognition (psychology); Natural language processing; Linguistics; Psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001193272,0.00006750961,0.0001091323,0.0001024863,0.00003909659,0.00003135694,0.0002308794,0.00004649143,0.00007321354],"category_scores_gemma":[0.00050194,0.00006224604,0.00003541675,0.0003960044,0.00001054117,0.0003224714,0.00001823115,0.00008589475,0.00002476918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004416709,"about_ca_system_score_gemma":0.00002944338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003510708,"about_ca_topic_score_gemma":0.00001430917,"domain_scores_codex":[0.999025,0.0001970725,0.0002484337,0.0002381194,0.0001766796,0.0001147274],"domain_scores_gemma":[0.999434,0.0001112845,0.00008122221,0.0002826659,0.00006415107,0.00002669078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000006389868,0.0002917523,0.01239611,0.000003899296,0.000003422396,6.145112e-7,0.0003197438,0.000007501877,0.004747007,0.02224866,0.0000444595,0.9599304],"study_design_scores_gemma":[0.0003269023,0.00004068391,0.8605185,0.00001767993,0.000002888652,0.000005994099,0.00003823947,0.02263363,0.09590846,0.02032015,0.00003825047,0.0001485777],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5797348,0.000006946932,0.4033925,0.0009345212,0.00005316615,0.0002762337,0.000001134138,0.00009340515,0.01550731],"genre_scores_gemma":[0.9401577,0.000006888281,0.05968563,0.00008521475,0.00001229509,0.000003542417,0.00000401189,0.000001848922,0.00004289226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9597819,"threshold_uncertainty_score":0.253832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01121997268864581,"score_gpt":0.2277804280340246,"score_spread":0.2165604553453788,"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."}}