{"id":"W2159615724","doi":"10.1109/vetec.1989.40140","title":"Performance of a low complexity CELP speech coder under mobile channel fading conditions","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Instituto de Telecomunicações","keywords":"Code-excited linear prediction; Computer science; Speech coding; Codec; Codebook; Vector sum excited linear prediction; Convolutional code; Residual; Speech recognition; Codec2; Full Rate; Coding (social sciences); Adaptive Multi-Rate audio codec; Linear predictive coding; Computer engineering; Computer hardware; Voice activity detection; Algorithm; Speech processing; Decoding methods","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":[0.0003904389,0.0005822548,0.0006167734,0.000340616,0.0003996891,0.0004609441,0.0004891782,0.0005620469,0.001178881],"category_scores_gemma":[0.00265159,0.0001707,0.0002568771,0.0002539738,0.0004791376,0.0003881903,0.0003124507,0.0003763076,0.000326315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005895413,"about_ca_system_score_gemma":0.000343307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006825028,"about_ca_topic_score_gemma":0.002603509,"domain_scores_codex":[0.9997049,0.0000690375,0.00001365665,0.00004254506,0.0001159167,0.00005387019],"domain_scores_gemma":[0.9983659,0.001136079,0.00009290576,0.00006834812,0.0002781033,0.00005855482],"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":[0.0011587,0.0001117417,0.003682519,0.0001281326,0.00007391758,0.0004878842,0.0001814983,0.9307515,0.04675441,0.0007064635,0.0005057587,0.01545764],"study_design_scores_gemma":[0.00007098508,0.0006193168,0.002002327,0.000006628331,0.00003454087,0.0001188522,0.00002875331,0.9572452,0.03943872,0.0001385997,0.0002776471,0.00001850805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9809568,0.0001449788,0.01581949,0.00009367934,0.00002134968,0.00003495354,0.000188515,0.0007360852,0.002004111],"genre_scores_gemma":[0.9970766,0.00004624166,0.002117384,0.00001550941,0.00000233281,0.00001028789,0.00009054335,0.00003708075,0.0006039523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006825028,"threshold_uncertainty_score":0.01357061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04012423959899859,"score_gpt":0.3013434345087496,"score_spread":0.261219194909751,"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."}}