{"id":"W4285102625","doi":"10.1109/dslw53931.2022.9820506","title":"Practical Cognitive Speech Compression","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Codec; Computer science; Speech recognition; Speech coding; PSQM; Encoder; Telephony; Quantization (signal processing); Linear predictive coding; Computer vision; 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":[0.0003771155,0.0004316858,0.0001908439,0.0003480795,0.0001987593,0.0004302592,0.0005098296,0.0003995116,0.002444245],"category_scores_gemma":[0.001367645,0.00009997644,0.0002067228,0.0002294953,0.0004237686,0.0005094026,0.0005630549,0.0005242284,0.0004462635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003681735,"about_ca_system_score_gemma":0.0004341744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002050623,"about_ca_topic_score_gemma":0.003858513,"domain_scores_codex":[0.9996884,0.00004948268,0.0000135593,0.00005223353,0.0001731983,0.00002312181],"domain_scores_gemma":[0.9995661,0.0001801154,0.00003196472,0.00008109637,0.0001252647,0.00001542076],"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":[0.0002481998,0.000116757,0.0007803763,0.0001993113,0.00004006532,0.0001934322,0.0001675654,0.08202454,0.1479266,0.02519226,0.002945567,0.7401654],"study_design_scores_gemma":[0.00002952131,0.000193821,0.001868323,0.00002675057,0.00002934111,0.0005805783,0.00005497358,0.8692318,0.1066963,0.01141763,0.009838217,0.00003287121],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04686053,0.000347571,0.9409471,0.0001695034,0.00006997045,0.0000688682,0.00007838614,0.000856064,0.01060199],"genre_scores_gemma":[0.5964519,0.0003152286,0.3950362,0.0001859598,0.00009305759,0.00008324272,0.0002201657,0.00007236117,0.007541807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002444245,"threshold_uncertainty_score":0.008176804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04486009747172853,"score_gpt":0.3631831777002367,"score_spread":0.3183230802285082,"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."}}