{"id":"W1943963003","doi":"10.1109/icassp.1984.1172363","title":"A new concept for encoding speech amplitude time quantization","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Speech coding; Quantization (signal processing); Analog signal; Analog transmission; Codec2; Linear predictive coding; Sub-band coding; Amplitude; Speech recognition; Sampling (signal processing); Algorithm; Transmission (telecommunications); Telecommunications; Physics; Optics","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":[0.0001086831,0.00009475634,0.0001109986,0.00006434305,0.00007470929,0.0001106408,0.000707767,0.00004067428,0.0001578],"category_scores_gemma":[0.00005722368,0.00008381472,0.00003638476,0.0001694947,0.00001144879,0.001043972,0.0002243278,0.00004573545,0.0001065221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003458876,"about_ca_system_score_gemma":0.00004220256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009890703,"about_ca_topic_score_gemma":0.000002627219,"domain_scores_codex":[0.9991559,0.00001484555,0.0001830197,0.000308076,0.0001490413,0.0001891397],"domain_scores_gemma":[0.9992304,0.00009398726,0.00006820956,0.000468018,0.00005979156,0.0000795579],"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":[0.00000326412,0.00001968693,0.000008759084,0.000002662412,0.0000033436,6.263966e-7,0.0001196162,0.0001457391,0.01874613,0.3139134,0.08615069,0.5808861],"study_design_scores_gemma":[0.0003615944,0.00006700737,0.0000257133,0.00002667784,0.00000262036,0.000009945137,0.000004520826,0.2638587,0.4127541,0.02005559,0.3025801,0.0002534684],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003141865,0.00004646689,0.9950693,0.0008761849,0.00005777222,0.0002664348,0.000003468912,0.0008558659,0.002793098],"genre_scores_gemma":[0.006585438,0.000005482799,0.9888197,0.0005772968,0.0001273856,0.00001377275,0.00001279777,0.00000804627,0.00385006],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5806326,"threshold_uncertainty_score":0.3417865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02242039720224218,"score_gpt":0.3091945715857677,"score_spread":0.2867741743835255,"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."}}