{"id":"W2577062905","doi":"10.1121/1.4973569","title":"Predicting phoneme and word recognition in noise using a computational model of the auditory periphery","year":2017,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"European Commission","keywords":"Filter bank; Computer science; Speech recognition; Psychoacoustics; Metric (unit); Benchmark (surveying); Intelligibility (philosophy); Artificial intelligence; Pattern recognition (psychology); Filter (signal processing); Perception; Psychology","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":[0.0005702635,0.0005921614,0.0004313048,0.0005463712,0.0001249925,0.0006177633,0.0002639629,0.0004393156,0.0006262664],"category_scores_gemma":[0.002398509,0.0001887021,0.0003213354,0.0002862992,0.0001855107,0.0007517962,0.0002906911,0.0003546464,0.0003682862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003047693,"about_ca_system_score_gemma":0.0004297128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002951183,"about_ca_topic_score_gemma":0.004307849,"domain_scores_codex":[0.9998678,0.00002984522,0.00001151854,0.00005021804,0.00002503904,0.00001556111],"domain_scores_gemma":[0.9993508,0.0004666879,0.00005232067,0.00003141063,0.00007308268,0.00002568594],"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":[0.0006611121,0.0002859322,0.03120486,0.0001304931,0.0001920304,0.0001822508,0.0002765166,0.6831558,0.07689129,0.002317799,0.0005721725,0.2041298],"study_design_scores_gemma":[0.000001647993,0.00005277288,0.003810138,0.000002739648,0.000009414312,0.00002340896,0.00001210206,0.9931675,0.002334339,0.0005340956,0.00004565043,0.00000629697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5918467,0.0002781464,0.4061626,0.00007366457,0.00003216866,0.00003378443,0.0001540626,0.000550067,0.0008687577],"genre_scores_gemma":[0.9526302,0.0001387826,0.04625421,0.00002104956,0.00001321378,0.00003622665,0.0001594097,0.00002111474,0.0007258459],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002951183,"threshold_uncertainty_score":0.005868018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04891521368304001,"score_gpt":0.2908878899350589,"score_spread":0.2419726762520189,"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."}}