{"id":"W2123314527","doi":"10.1109/icwc.1992.200742","title":"Combined speech and channel coding for mobile radio applications","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Code-excited linear prediction; Computer science; Speech recognition; Vector sum excited linear prediction; Convolutional code; Speech coding; Channel (broadcasting); Algorithm; Linear predictive coding; Decoding methods; Telecommunications","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":[0.0002108079,0.0003600463,0.0002191219,0.0005888203,0.000171049,0.000474443,0.0003956921,0.0004710183,0.006044379],"category_scores_gemma":[0.0007107919,0.0001274387,0.0001832914,0.0004468469,0.000257913,0.0005043398,0.0005740673,0.0004586971,0.002651999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002563562,"about_ca_system_score_gemma":0.0003467661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00118454,"about_ca_topic_score_gemma":0.002706594,"domain_scores_codex":[0.9996864,0.00005165355,0.00001023367,0.00002634766,0.0001928028,0.00003258643],"domain_scores_gemma":[0.99954,0.0001206664,0.00002156067,0.00007258753,0.0002242802,0.00002099089],"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":[0.0006367788,0.0001064648,0.0005610394,0.0003648623,0.00009108135,0.0004729995,0.0001206357,0.03221514,0.1435861,0.0436188,0.02288011,0.7553461],"study_design_scores_gemma":[0.0001259121,0.0006345735,0.001581163,0.0001468792,0.0001387605,0.001707525,0.00007372883,0.6319023,0.1877147,0.02753989,0.1483479,0.00008671288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03955555,0.007002789,0.9076326,0.0007690377,0.0009116376,0.0001309093,0.0003560183,0.002191643,0.04144992],"genre_scores_gemma":[0.5875027,0.004067296,0.3256112,0.0008143763,0.0006775951,0.0002366207,0.001106377,0.0002588394,0.07972489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006044379,"threshold_uncertainty_score":0.02022052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01339647524794178,"score_gpt":0.2548465321552534,"score_spread":0.2414500569073117,"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."}}