{"id":"W2132572274","doi":"10.1109/tsa.2005.851917","title":"LSP quantization by a union of locally trained codebooks","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Codebook; Vector quantization; Algorithm; Mathematics; Encoder; Speech coding; Computational complexity theory; Code-excited linear prediction; Speech recognition; Pattern recognition (psychology); Linear predictive coding; Computer science; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00118314,0.0005381792,0.0007995916,0.00041181,0.0003084451,0.0007502843,0.001513185,0.0006883538,0.003387571],"category_scores_gemma":[0.004050131,0.0003895617,0.000425965,0.0007312929,0.0006800791,0.001912384,0.001267092,0.001186881,0.001857515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005679436,"about_ca_system_score_gemma":0.000660971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002167047,"about_ca_topic_score_gemma":0.002500724,"domain_scores_codex":[0.998968,0.0002591466,0.00006488949,0.0001986764,0.0004543312,0.00005495809],"domain_scores_gemma":[0.9987894,0.0003750206,0.00008447191,0.0003864729,0.0003199825,0.0000447737],"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":[0.0003730201,0.00009371246,0.0005050057,0.0001312464,0.00005364954,0.00007352538,0.0001486506,0.4807514,0.03602455,0.04298704,0.005475794,0.4333824],"study_design_scores_gemma":[0.00001625748,0.00003714247,0.00007531137,0.000007655439,0.000004555343,0.00004047985,0.000006625558,0.9885344,0.005921134,0.004146924,0.001200124,0.000009423015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003853699,0.00009479481,0.9949573,0.00003997937,0.00001608596,0.00001872136,0.00003309602,0.0004018549,0.0005845184],"genre_scores_gemma":[0.2187703,0.0002073241,0.775421,0.0001473629,0.00006056332,0.000200785,0.0003493326,0.0001914195,0.004651871],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003387571,"threshold_uncertainty_score":0.01133257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01246671299897269,"score_gpt":0.2608341671625037,"score_spread":0.248367454163531,"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."}}