{"id":"W2131695036","doi":"10.1109/glocom.1990.116643","title":"Low-delay analysis-by-synthesis speech coding using lattice predictors","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Codebook; Codec; Speech coding; Lattice phase equaliser; Computer science; Speech recognition; Algorithm; Linear predictive coding; Intelligibility (philosophy); Lattice (music); Term (time); Weighting; Coding (social sciences); Adaptive filter; Mathematics; Statistics; Telecommunications; Acoustics; 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":[0.0002449834,0.000198315,0.0002929117,0.0003341869,0.0001789882,0.0001819693,0.001338981,0.00008429513,0.0006917357],"category_scores_gemma":[0.0001309845,0.0001730514,0.0001235911,0.001511008,0.00004839131,0.001260417,0.0005560063,0.000151787,0.00006013382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007394631,"about_ca_system_score_gemma":0.000008867929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000385174,"about_ca_topic_score_gemma":0.000003714028,"domain_scores_codex":[0.9981708,0.00009178495,0.0003404438,0.0005918184,0.0004443669,0.0003607876],"domain_scores_gemma":[0.9981682,0.0003144786,0.0001454989,0.001144581,0.0000784679,0.000148732],"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":[0.00002505838,0.001850997,0.01681374,0.000154875,0.002222967,0.0004699577,0.001346367,0.00571572,0.1705231,0.05036962,0.2157493,0.5347583],"study_design_scores_gemma":[0.00007072417,0.00001566953,0.00008047437,0.00003113713,0.00009868548,0.00001276489,0.00001013326,0.7870121,0.207639,0.0003987141,0.00433774,0.0002928912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00894334,0.00008413167,0.9856524,0.0001487058,0.00008638201,0.0001122151,0.00001580654,0.0009538934,0.004003137],"genre_scores_gemma":[0.4239052,0.00005688802,0.5750794,0.0002434941,0.00003198553,0.00001140873,0.000003187254,0.00001383189,0.0006545545],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7812964,"threshold_uncertainty_score":0.757402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03144634991494086,"score_gpt":0.275207387040827,"score_spread":0.2437610371258862,"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."}}