{"id":"W2153900043","doi":"10.1109/icassp.1979.1170773","title":"Automatic discrimination of fricative consonants based on human audition","year":2005,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Discriminator; Speech recognition; Octave (electronics); Computer science; Band-pass filter; Spectral analysis; Set (abstract data type); Auditory system; Mathematics; Artificial intelligence; Acoustics; Audiology; Detector; Physics","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":[0.0001067081,0.00005612523,0.00007269906,0.00008762881,0.00007563417,0.00003309315,0.0002060706,0.00001946139,0.00009383578],"category_scores_gemma":[0.00003125133,0.00004503243,0.00002254896,0.0001667997,0.00002650526,0.0003128508,0.00002033007,0.00003277497,0.000038583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003349297,"about_ca_system_score_gemma":0.00003059598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003067737,"about_ca_topic_score_gemma":0.000005955021,"domain_scores_codex":[0.999429,0.0000236489,0.0001327451,0.0001399674,0.0001834414,0.00009118061],"domain_scores_gemma":[0.9995891,0.00005302558,0.000104912,0.0001710912,0.00005461932,0.00002718654],"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":[0.000002488842,0.0004008565,0.0004150593,0.00006409381,0.000006753184,0.000001666822,0.0009873419,0.0002759665,0.03419441,0.0252586,0.002811337,0.9355814],"study_design_scores_gemma":[0.0003004623,0.0001025398,0.01560491,0.0000958977,0.000003618186,8.265112e-7,0.00003295464,0.3324044,0.6478079,0.003407077,0.0001437931,0.00009567224],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1406431,0.00001305425,0.8300094,0.003544047,0.00004155985,0.000107572,0.000001466148,0.0001854823,0.02545435],"genre_scores_gemma":[0.8982014,3.233367e-7,0.1012208,0.0003872138,0.0000164833,0.000004995326,0.000002494168,0.000002223951,0.0001640315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9354857,"threshold_uncertainty_score":0.1836369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03332959157151707,"score_gpt":0.3071272995488322,"score_spread":0.2737977079773152,"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."}}