{"id":"W1864449204","doi":"10.1109/asru.2001.1034611","title":"Joint estimation of noise and channel distortion in a generalized EM framework","year":2005,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Initialization; Computer science; Noise (video); Channel (broadcasting); Distortion (music); Focus (optics); Algorithm; Joint (building); Convergence (economics); Noise measurement; Noise reduction; Speech recognition; Artificial intelligence; Telecommunications; Image (mathematics); Engineering; Bandwidth (computing)","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":[0.001524995,0.0007492811,0.0008712685,0.0004222586,0.0002257786,0.0007794006,0.001155626,0.000979802,0.0007968981],"category_scores_gemma":[0.004082686,0.0004178139,0.000618916,0.0005852172,0.0008612383,0.001358276,0.001224646,0.001111075,0.000446885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003782481,"about_ca_system_score_gemma":0.0006858119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002303402,"about_ca_topic_score_gemma":0.002736125,"domain_scores_codex":[0.9993336,0.0002982067,0.00002981079,0.0001483824,0.0001427086,0.00004734818],"domain_scores_gemma":[0.9991215,0.000499915,0.00008271213,0.0001664949,0.0001094006,0.00001998502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001390497,0.00003875348,0.0006789307,0.0001115657,0.0001062265,0.0001036024,0.00008156727,0.8093168,0.01213141,0.02764525,0.0009176498,0.1487292],"study_design_scores_gemma":[0.00001095946,0.00001795616,0.0001959909,0.000005119741,0.00000952441,0.00006156736,0.000009946071,0.9852203,0.004100133,0.009611796,0.0007443611,0.00001237292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004196151,0.00005725426,0.9952865,0.00004681847,0.000006618788,0.000007892092,0.00001232716,0.0001752316,0.0002111438],"genre_scores_gemma":[0.2025061,0.0003584459,0.7939198,0.000146125,0.00004910106,0.00008114241,0.0002197814,0.0001620328,0.002557512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002303402,"threshold_uncertainty_score":0.008065045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01583150335592185,"score_gpt":0.2503776848814325,"score_spread":0.2345461815255107,"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."}}