{"id":"W2153038597","doi":"10.1109/msp.2014.2358871","title":"Objective Quality and Intelligibility Prediction for Users of Assistive Listening Devices: Advantages and limitations of existing tools","year":2015,"lang":"en","type":"article","venue":"IEEE Signal Processing Magazine","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":138,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Institut National de la Recherche Scientifique","funders":"National Institute on Deafness and Other Communication Disorders","keywords":"Reverberation; Intelligibility (philosophy); Active listening; Computer science; Speech perception; Speech recognition; Cochlear implant; Hearing aid; Perception; Sound quality; Audiology; Acoustics; Psychology; Medicine","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":[0.001087994,0.0001493484,0.0003099457,0.0001112023,0.0001413471,0.000139128,0.0001738377,0.0000616778,2.474678e-7],"category_scores_gemma":[0.001316384,0.000138536,0.0000371917,0.0003308686,0.0002165903,0.001255983,0.00007178822,0.0001070919,2.334159e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004098152,"about_ca_system_score_gemma":0.000277729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009528254,"about_ca_topic_score_gemma":0.00002097673,"domain_scores_codex":[0.9985906,0.00006079632,0.0005047054,0.000407468,0.0002500344,0.0001864071],"domain_scores_gemma":[0.9974009,0.0009693459,0.0005223491,0.0001319258,0.0008683168,0.000107184],"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":[0.0002027357,0.0001262215,0.02321976,0.001390169,0.00004681596,0.000001663072,0.005723508,0.0004791585,0.07253768,0.0004120703,0.00003837347,0.8958218],"study_design_scores_gemma":[0.00385869,0.002058858,0.2051383,0.002853972,0.0002385334,0.00006886965,0.006004132,0.139331,0.6092873,0.0295561,0.0005470073,0.001057284],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2837692,0.0007841174,0.7138451,0.00008042934,0.00006267038,0.0002121455,0.00002432322,0.00006388008,0.001158155],"genre_scores_gemma":[0.8651604,0.00001190915,0.1346921,0.00003681149,0.00004243752,0.00001188632,0.00000473363,0.000008196779,0.0000315089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8947645,"threshold_uncertainty_score":0.5649335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.195169606849618,"score_gpt":0.3647818179845549,"score_spread":0.169612211134937,"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."}}