{"id":"W2110440191","doi":"10.1109/lsp.2005.845604","title":"Nonintrusive speech quality evaluation using an adaptive neurofuzzy inference system","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Oticon Fonden","keywords":"Adaptive neuro fuzzy inference system; Inference; Computer science; Fuzzy inference system; Quality (philosophy); Speech recognition; Standard error; Correlation; Standard system; Artificial intelligence; Pattern recognition (psychology); Data mining; Machine learning; Fuzzy logic; Fuzzy control system; Statistics; Mathematics; Engineering","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":[0.000605917,0.0001988544,0.000190485,0.0001224486,0.0002064841,0.0001230285,0.0003127105,0.00006509532,0.00001445496],"category_scores_gemma":[0.00001788765,0.0002113724,0.00004468154,0.0002566721,0.00005947278,0.0007200987,0.00002460594,0.0002764686,0.00002552097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003380727,"about_ca_system_score_gemma":0.00009069107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005330175,"about_ca_topic_score_gemma":0.00002275896,"domain_scores_codex":[0.9985726,0.000190302,0.0003657633,0.0002375648,0.0003636279,0.0002701234],"domain_scores_gemma":[0.9991781,0.0000681127,0.000114182,0.0003520015,0.0001959845,0.00009161176],"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":[0.00001910078,0.00004155767,0.0000912579,0.00009479584,0.00001882929,0.000003859051,0.0007316753,0.6810985,0.2331817,0.00003983641,0.00008731348,0.08459158],"study_design_scores_gemma":[0.0002509876,0.00002148212,0.0004767982,0.0001507101,0.00004327133,0.00001299719,0.000157932,0.9803789,0.01809819,0.00002129362,0.0001148808,0.0002725345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.879558,0.0002259252,0.1185425,0.0001691323,0.000145702,0.0002056196,0.000007254897,0.0003334052,0.0008124487],"genre_scores_gemma":[0.987489,0.000002967833,0.01181689,0.0003034168,0.0003154317,0.00002122917,0.00001006755,0.00003790043,0.000003075632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2992805,"threshold_uncertainty_score":0.8619517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08097163783514222,"score_gpt":0.3325286239090474,"score_spread":0.2515569860739051,"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."}}