{"id":"W1971872909","doi":"10.1016/j.specom.2015.02.001","title":"Objective measures for quality assessment of noise-suppressed speech","year":2015,"lang":"en","type":"article","venue":"Speech Communication","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Swenson College of Science and Engineering, University of Minnesota Duluth; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Speech recognition; Speech enhancement; Distortion (music); PESQ; Noise (video); Active listening; Residual; PSQM; Speech processing; Speech coding; Quality (philosophy); Background noise; Noise reduction; Speech perception; Voice activity detection; Artificial intelligence; Perception; Algorithm; Psychology; Telecommunications","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":[0.002299632,0.0001381957,0.0002664135,0.0001030847,0.0001464567,0.0001218872,0.001676625,0.00008190829,0.000002790541],"category_scores_gemma":[0.0005416315,0.000133684,0.00008765596,0.0003692931,0.0000818302,0.0006249116,0.0003746255,0.0001653497,0.000005555904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001469985,"about_ca_system_score_gemma":0.00037866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001464321,"about_ca_topic_score_gemma":0.00006711562,"domain_scores_codex":[0.9982392,0.0002997413,0.000422063,0.0002828153,0.0005348923,0.0002212636],"domain_scores_gemma":[0.9965823,0.0003114124,0.0003802421,0.001615644,0.00100631,0.0001040412],"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":[0.0001442132,0.001138169,0.01333117,0.0002612821,0.0001930119,0.000002707921,0.005300456,0.0005390358,0.1768546,0.05749653,0.005991304,0.7387475],"study_design_scores_gemma":[0.001464077,0.0001857814,0.01219708,0.00008919135,0.00002093107,0.000008411301,0.000461269,0.007040872,0.9099165,0.06403054,0.004261112,0.000324185],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08645426,0.0008243516,0.8807885,0.002604931,0.0002555735,0.0007864938,0.00001553866,0.0002557899,0.02801456],"genre_scores_gemma":[0.5703635,0.00001960005,0.4293894,0.00007995018,0.00002195224,0.00003307713,0.00001398,0.000007351243,0.0000711494],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7384233,"threshold_uncertainty_score":0.5451477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1001163082351362,"score_gpt":0.3762995965308839,"score_spread":0.2761832882957477,"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."}}