{"id":"W1671380688","doi":"10.1109/icassp.1994.389709","title":"8 kbit/s ACELP coding of speech with 10 ms speech-frame: a candidate for CCITT standardization","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Speech coding; Computer science; Linear predictive coding; Code-excited linear prediction; Codec2; Full Rate; Speech recognition; Codec; Adaptive Multi-Rate audio codec; Digital signal processing; Voice activity detection; Codebook; Vector sum excited linear prediction; Frame (networking); Quantization (signal processing); Coding (social sciences); Speech processing; Computer hardware; Algorithm; Mathematics; 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":[0.0009366795,0.0003304342,0.0002250081,0.0006808031,0.0004399264,0.0007840826,0.0006956516,0.0007661158,0.005022882],"category_scores_gemma":[0.001408827,0.00007364097,0.0001336185,0.0003880426,0.0004832284,0.0004672498,0.0003758509,0.0006260966,0.001561846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000527787,"about_ca_system_score_gemma":0.0007911081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002262134,"about_ca_topic_score_gemma":0.003373714,"domain_scores_codex":[0.9996226,0.0000671413,0.00002775891,0.00003977459,0.0001825852,0.00006021114],"domain_scores_gemma":[0.9989251,0.000127218,0.00005001553,0.0001152845,0.0006757147,0.0001066124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001864193,0.0002878896,0.003475462,0.0002174697,0.00003396948,0.001379141,0.0003552034,0.006951962,0.4056926,0.02830178,0.01461844,0.536822],"study_design_scores_gemma":[0.0002681708,0.002527648,0.007366528,0.00009873816,0.00008319291,0.003093084,0.0003181313,0.1202542,0.7474176,0.005934667,0.1125039,0.0001342196],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3141242,0.0008381985,0.6444303,0.001916995,0.0004860216,0.0005242422,0.0005416489,0.005514276,0.03162409],"genre_scores_gemma":[0.7083595,0.0003381863,0.2579596,0.0004113363,0.0001646438,0.0002403428,0.001528068,0.0003004001,0.03069792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005022882,"threshold_uncertainty_score":0.01680321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02005851192984123,"score_gpt":0.2706704139131948,"score_spread":0.2506119019833536,"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."}}