{"id":"W2111850347","doi":"10.1109/wescan.1993.270516","title":"Codebook searching for 4.8 kbps CELP speech coder","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"","keywords":"Code-excited linear prediction; Codebook; Computer science; Speech coding; Linear predictive coding; Vector sum excited linear prediction; Speech recognition; Linear prediction; Voice activity detection; Coding (social sciences); Codec2; Speech processing; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001796351,0.000116859,0.0001279097,0.00008576882,0.0001501465,0.0001252781,0.001128736,0.0000437159,0.000213504],"category_scores_gemma":[0.00005710945,0.00009717842,0.00005175527,0.0001277126,0.00003205182,0.0006952684,0.0004953786,0.0001187824,0.0001277896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002597759,"about_ca_system_score_gemma":0.000009899706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007622149,"about_ca_topic_score_gemma":0.000003231647,"domain_scores_codex":[0.998855,0.0000317535,0.0001756591,0.0003884115,0.0002346423,0.0003145193],"domain_scores_gemma":[0.9987879,0.0002142231,0.00004720236,0.0007801445,0.00007222767,0.00009824173],"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":[0.000003645842,0.00007640375,0.00002900309,0.00001833365,0.000007300928,0.00001161741,0.0002054268,0.00002610636,0.005485343,0.2098884,0.1916677,0.5925807],"study_design_scores_gemma":[0.0003352498,0.00008853613,0.00002036113,0.0000280712,0.000001988171,0.00002461895,0.000009713192,0.4355709,0.1750744,0.0468302,0.341723,0.0002929537],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0000673286,0.00008277229,0.9741148,0.0008544124,0.00009181922,0.0003325004,0.000007825007,0.0009214906,0.02352702],"genre_scores_gemma":[0.01698382,0.00002191382,0.9710179,0.0008650872,0.00004933643,0.00006386498,0.000003347943,0.00001427796,0.01098044],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5922878,"threshold_uncertainty_score":0.3962821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05537091830909333,"score_gpt":0.2992801382880668,"score_spread":0.2439092199789734,"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."}}