{"id":"W177255557","doi":"10.21437/interspeech.2004-99","title":"Noise adaptation for robust AURORA 2 noisy digit recognition using statistical data mapping","year":2004,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec","funders":"","keywords":"Computer science; Speech recognition; Noise (video); Adaptation (eye); Digit recognition; Feature (linguistics); Pattern recognition (psychology); Artificial intelligence; Noisy data; Noise measurement; Feature extraction; Noise reduction; Artificial neural network","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":[0.0006739644,0.0004457953,0.000521887,0.0003349255,0.0001853254,0.0003699663,0.0004326721,0.0003383666,0.0009810111],"category_scores_gemma":[0.001727644,0.0002069453,0.0004209559,0.000365692,0.0002827049,0.0004018864,0.0005059034,0.0004666371,0.0005747741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001902683,"about_ca_system_score_gemma":0.0003646973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009936383,"about_ca_topic_score_gemma":0.001272161,"domain_scores_codex":[0.9995105,0.0001643349,0.0000329718,0.0001026566,0.000161369,0.00002809956],"domain_scores_gemma":[0.9996604,0.0001348838,0.00002861718,0.00007321451,0.00009020099,0.00001271895],"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":[0.0005455089,0.0001200683,0.001353816,0.000109748,0.00007153628,0.0001480551,0.00009637715,0.07950284,0.1903395,0.002637163,0.00163661,0.7234387],"study_design_scores_gemma":[0.00002387353,0.0001665894,0.002618414,0.000006791128,0.00002193644,0.0002661299,0.00002389558,0.9241411,0.06818594,0.001243043,0.003270062,0.00003226041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05616793,0.000288815,0.9410357,0.00007434314,0.00005470752,0.00003002887,0.00006108376,0.001694851,0.00059244],"genre_scores_gemma":[0.5318409,0.0002505645,0.4653174,0.00007342139,0.0000545429,0.0001224485,0.0004313857,0.0001574838,0.001751969],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009936383,"threshold_uncertainty_score":0.003564298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.244781941041828,"score_gpt":0.3129141343636097,"score_spread":0.06813219332178161,"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."}}