{"id":"W3106864934","doi":"10.1109/taslp.2020.3039929","title":"Speech Intelligibility Prediction Using Spectro-Temporal Modulation Analysis","year":2020,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Audio Speech and Language Processing","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Institute on Deafness and Other Communication Disorders; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Speech recognition; Computer science; Intelligibility (philosophy); Reverberation; Speech perception; Active listening; Speech processing; Auditory scene analysis; Perception; Robustness (evolution); Acoustics; Psychology; Communication","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001125359,0.0007642221,0.0004417946,0.0006842786,0.0001698779,0.0005943793,0.0003472635,0.0004799419,0.0006378326],"category_scores_gemma":[0.004227968,0.0001707506,0.00050345,0.000281746,0.0002759479,0.0006824668,0.0006781184,0.0008361086,0.0003360031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000178607,"about_ca_system_score_gemma":0.0003616602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009175289,"about_ca_topic_score_gemma":0.001271744,"domain_scores_codex":[0.9997093,0.00008327604,0.00002527106,0.00008303148,0.00007168131,0.00002743229],"domain_scores_gemma":[0.9991459,0.0004836345,0.00010122,0.00006023237,0.0001665139,0.00004251624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001020754,0.0003741425,0.01900855,0.0002121244,0.0002180253,0.000242304,0.0002771221,0.2495031,0.08637677,0.001980582,0.003360488,0.637426],"study_design_scores_gemma":[0.00001326057,0.0001391467,0.006105879,0.00001328362,0.00003427032,0.00008005479,0.00003716993,0.9818411,0.009977807,0.001289788,0.000449903,0.00001827165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3216367,0.0007123035,0.6738841,0.0003783533,0.00008080609,0.00009196488,0.000354826,0.001157588,0.001703371],"genre_scores_gemma":[0.8991343,0.0002504084,0.0984462,0.00007675469,0.00007845664,0.0001000402,0.0007213177,0.00007006178,0.001122442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001125359,"threshold_uncertainty_score":0.005951583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04441023226516962,"score_gpt":0.3101454051016216,"score_spread":0.265735172836452,"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."}}