{"id":"W1513826108","doi":"10.1109/wescan.1991.160550","title":"A neural network mapper for stochastic code book parameter encoding in code-excited linear predictive speech processing","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"","keywords":"Code-excited linear prediction; Computer science; Artificial neural network; Speech recognition; Vector sum excited linear prediction; Linear predictive coding; Code (set theory); Encoding (memory); Coding (social sciences); Speech coding; Source code; Artificial intelligence; Programming language; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002844246,0.0003556674,0.0002551443,0.0003237715,0.0003105752,0.0004624386,0.0008395975,0.0006751415,0.004739987],"category_scores_gemma":[0.0009390066,0.0002052072,0.0001585139,0.0003259789,0.0002783864,0.0007840755,0.0005124515,0.0006404912,0.001466326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000391263,"about_ca_system_score_gemma":0.0003635863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00147069,"about_ca_topic_score_gemma":0.002444395,"domain_scores_codex":[0.9998542,0.00003212103,0.000007929975,0.00002718724,0.00006843884,0.0000100412],"domain_scores_gemma":[0.9998268,0.00006787435,0.0000152457,0.0000349904,0.00004418037,0.00001084227],"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":[0.0002381592,0.0001086262,0.0003094326,0.0001337689,0.00004111933,0.0002381658,0.0001108293,0.05445926,0.0997609,0.02578199,0.006571916,0.8122458],"study_design_scores_gemma":[0.00002443467,0.00006415625,0.0001876248,0.00001434819,0.00001066317,0.0001900135,0.00000998111,0.9273856,0.05779501,0.00485694,0.009445439,0.00001583262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008753608,0.0001746238,0.9866588,0.0001369636,0.00008183027,0.00006085031,0.00005685422,0.002008836,0.002067593],"genre_scores_gemma":[0.1794649,0.0001699676,0.8108544,0.0001638524,0.00006063862,0.0001732641,0.0001738515,0.0001997992,0.00873938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004739987,"threshold_uncertainty_score":0.01585686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03902769467448234,"score_gpt":0.273069572611193,"score_spread":0.2340418779367107,"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."}}