{"id":"W2009065571","doi":"10.1155/2012/172625","title":"Multiengine Speech Processing Using SNR Estimator in Variable Noisy Environments","year":2012,"lang":"en","type":"article","venue":"Advances in Acoustics and Vibration","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Algorithm; Estimator; Computer science; Artificial intelligence; Signal processing; Speech recognition; Mathematics; Statistics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001815384,0.000103131,0.0001098372,0.0001017004,0.00008598271,0.00009530197,0.0001195988,0.00004851516,0.000002271202],"category_scores_gemma":[0.00004799556,0.0001016008,0.000006550733,0.000289424,0.0000251152,0.002855319,0.00007005128,0.0000975744,0.000001551419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005531998,"about_ca_system_score_gemma":0.00002829466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004975959,"about_ca_topic_score_gemma":0.00000250678,"domain_scores_codex":[0.9991816,0.00001538278,0.0001975408,0.0001947701,0.0001375055,0.0002732037],"domain_scores_gemma":[0.999715,0.0000323678,0.00008336175,0.0001042687,0.00001027706,0.00005471056],"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":[0.00001332125,0.0002982693,0.06459847,0.0002199954,0.000003005026,0.00001262167,0.0008973558,0.1394075,0.2863989,0.001128222,0.000004595677,0.5070177],"study_design_scores_gemma":[0.0003468442,0.00001832459,0.003833197,0.0001228261,0.00000376407,0.00001376073,0.00006835123,0.9687569,0.02500282,0.001333427,0.0003266396,0.0001732095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1066971,0.002333996,0.8905503,0.0000200736,0.0001688086,0.00007958386,7.942604e-7,0.00001876945,0.0001305704],"genre_scores_gemma":[0.5582367,0.0001077479,0.4415509,0.00004664911,0.00004349798,0.000002991011,0.00000171354,0.000004655189,0.000005205068],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8293493,"threshold_uncertainty_score":0.414316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01076729916403197,"score_gpt":0.2665742648380403,"score_spread":0.2558069656740083,"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."}}