{"id":"W2159771978","doi":"10.1109/icassp.1996.543231","title":"Switched prediction and quantization of LSP frequencies","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Quantization (signal processing); Vector quantization; Algorithm; Residual; Speech coding; Coding (social sciences); Harmonic Vector Excitation Coding; Mathematics; Computer science; Speech recognition; Statistics","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":[0.00005941331,0.00004687604,0.00006531677,0.0000610663,0.00003087602,0.00001968981,0.0002020341,0.00002700081,0.00003864674],"category_scores_gemma":[0.00003524557,0.00003877475,0.000009286032,0.0001559472,0.00002872104,0.0006883955,0.0001164881,0.00003105737,0.000003403596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000637737,"about_ca_system_score_gemma":0.000002672824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001072262,"about_ca_topic_score_gemma":0.000001248382,"domain_scores_codex":[0.9995276,0.0000154265,0.0001311303,0.0001475635,0.0001150295,0.0000632786],"domain_scores_gemma":[0.9995624,0.00003042209,0.0000566034,0.0002788389,0.00004762825,0.00002414592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002933097,0.0001225237,0.006846496,0.00004656659,0.00001110394,0.000002518605,0.0009441007,0.00006712615,0.269505,0.5017356,0.01457703,0.206139],"study_design_scores_gemma":[0.000272323,0.0001659177,0.007370234,0.0000524605,0.000004043365,0.00001722922,0.00003802159,0.4857882,0.4665567,0.0351432,0.004411491,0.0001801542],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005997363,0.0001466193,0.9911245,0.0001606877,0.00004060009,0.00006613031,0.00000278624,0.000311696,0.002149684],"genre_scores_gemma":[0.7117133,0.0001410316,0.2879362,0.00004815566,0.000006864095,0.000004485129,0.000001589213,0.000002300409,0.00014601],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.705716,"threshold_uncertainty_score":0.1581188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02762227362269744,"score_gpt":0.2413408913193714,"score_spread":0.2137186176966739,"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."}}