{"id":"W1518423664","doi":"10.1109/glocom.1988.25850","title":"A vector ADPCM analysis-by-synthesis configuration for 16 kbit/s speech coding","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Vector quantization; Coding (social sciences); Speech coding; Algorithm; Vector sum excited linear prediction; Quantization (signal processing); Speech recognition; Theoretical computer science; Linear predictive coding; Mathematics; Code-excited linear prediction","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":[0.0003249672,0.0001452005,0.0002386972,0.0001914626,0.0001398816,0.0001506387,0.0006282001,0.00006400597,0.0002745943],"category_scores_gemma":[0.000436552,0.0001253984,0.000117602,0.0006803159,0.000022599,0.0006770716,0.00007436462,0.00006343028,0.00002173714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007055592,"about_ca_system_score_gemma":0.0000341111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001731676,"about_ca_topic_score_gemma":0.00001270365,"domain_scores_codex":[0.9987139,0.00007871303,0.0002694268,0.0004653978,0.0002250237,0.0002475684],"domain_scores_gemma":[0.9984868,0.0004475834,0.0001273896,0.0007285652,0.0001249688,0.00008475537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001888671,0.0001771208,0.0002411922,0.0000296382,0.0002468328,0.000004591955,0.00008983396,0.00005799386,0.1132684,0.6485742,0.1236496,0.1136417],"study_design_scores_gemma":[0.0001317435,0.00003508423,0.00003966286,0.00001099829,0.00004882041,0.000002618068,0.0000155187,0.01883846,0.91499,0.006953332,0.05870935,0.0002244045],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002101963,0.00004499824,0.9918216,0.0002456167,0.00007827361,0.0002930923,0.00002525509,0.0004688879,0.00681208],"genre_scores_gemma":[0.4008511,0.00001562851,0.5971583,0.0002950307,0.00001707392,0.0001725499,0.0000229832,0.00001028468,0.00145707],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8017216,"threshold_uncertainty_score":0.5113599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0216024804566681,"score_gpt":0.2877910873465661,"score_spread":0.266188606889898,"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."}}