{"id":"W2463893821","doi":"10.1121/1.4954254","title":"Predicting binaural speech intelligibility using the signal-to-noise ratio in the envelope power spectrum domain","year":2016,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Danmarks Tekniske Universitet","keywords":"Binaural recording; Monaural; Acoustics; Weighting; Speech recognition; Reverberation; Intelligibility (philosophy); Impulse response; Computer science; Envelope (radar); Interaural time difference; Noise (video); Mathematics; Physics; Radar; Telecommunications; Artificial intelligence; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000958406,0.0008670978,0.0006523339,0.0006213821,0.0001293678,0.0007576618,0.0004282182,0.0006791552,0.0006314893],"category_scores_gemma":[0.002547153,0.0003401012,0.0007275701,0.0002698589,0.0001807883,0.001042901,0.0005405789,0.0005013059,0.0004602468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002111682,"about_ca_system_score_gemma":0.0003847283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002390239,"about_ca_topic_score_gemma":0.001980204,"domain_scores_codex":[0.9997213,0.00008012029,0.00001729746,0.00008119579,0.00007437314,0.00002569277],"domain_scores_gemma":[0.9991462,0.0005660497,0.00006760644,0.00008739159,0.0001046392,0.00002795239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000826909,0.0004615985,0.02104076,0.0003081622,0.0002983169,0.0002354146,0.0002088997,0.7035904,0.09185342,0.00111838,0.0003608401,0.1796969],"study_design_scores_gemma":[0.000007917302,0.0001581634,0.006760966,0.000007586314,0.00003149842,0.00009783815,0.00001828712,0.9835073,0.008718119,0.000475909,0.0001977737,0.00001863895],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5515848,0.0004344499,0.4455079,0.00008287495,0.0000426234,0.00005709998,0.0001859255,0.0009706391,0.0011337],"genre_scores_gemma":[0.9581413,0.0002123377,0.04066534,0.00002705382,0.00000870703,0.00003807484,0.0002022501,0.00004406581,0.0006608851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002390239,"threshold_uncertainty_score":0.00506866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02462648677502165,"score_gpt":0.2903319941997847,"score_spread":0.265705507424763,"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."}}