{"id":"W2032556581","doi":"10.1121/1.2108861","title":"Measuring the acoustic effects of compression amplification on speech in noise","year":2006,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute on Deafness and Other Communication Disorders","keywords":"QUIET; Acoustics; Noise (video); Background noise; Computer science; Speech recognition; Dynamic range; Physics; Artificial intelligence","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":[0.0003327216,0.0003670777,0.0002487667,0.0003305599,0.0002572481,0.0003005037,0.000263147,0.0003946796,0.001148968],"category_scores_gemma":[0.001742315,0.0001692556,0.0001311912,0.0001887298,0.0004307361,0.0003924587,0.0003984008,0.0003336587,0.000362189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002018708,"about_ca_system_score_gemma":0.000198368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004290129,"about_ca_topic_score_gemma":0.0005837142,"domain_scores_codex":[0.9994264,0.000101514,0.0000240527,0.00007965233,0.000315665,0.0000526525],"domain_scores_gemma":[0.9990913,0.0005361834,0.00007166535,0.0000519432,0.0001949352,0.00005395133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002994018,0.00002891948,0.0008427238,0.0000509006,0.000006679763,0.00008008132,0.00007552928,0.0003096249,0.9856873,0.0000762795,0.00002345293,0.01251913],"study_design_scores_gemma":[0.00001120861,0.0006722739,0.008406437,0.000007137223,0.00003081909,0.0004331627,0.00006531698,0.003414995,0.9862186,0.00008644393,0.000641113,0.00001262892],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779002,0.0005541396,0.01909925,0.00004085102,0.00004220521,0.00004811306,0.0000425994,0.00009977962,0.002172895],"genre_scores_gemma":[0.9805049,0.000530803,0.01742877,0.00005826643,0.00004934022,0.00003998682,0.00007401407,0.00002536699,0.001288586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001148968,"threshold_uncertainty_score":0.003843665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01244227588007775,"score_gpt":0.2360477618239906,"score_spread":0.2236054859439129,"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."}}