{"id":"W2022826584","doi":"10.1121/1.4788580","title":"Is speech lazy or just efficient? A control-theoretic analysis","year":2005,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Vocal tract; Computer science; Speech production; Articulator; Energy (signal processing); Computation; Utterance; Set (abstract data type); Measure (data warehouse); Control (management); Phonation; Algorithm; Control theory (sociology); Speech recognition; Artificial intelligence; Mathematics; Data mining","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":[0.002164575,0.0005448236,0.0007890098,0.001061986,0.0006116643,0.002945072,0.0009795126,0.00104626,0.004859861],"category_scores_gemma":[0.01004532,0.000360171,0.0007873204,0.0005050631,0.004623177,0.004454599,0.001154308,0.001084903,0.0004740095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001295213,"about_ca_system_score_gemma":0.0006618176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001110777,"about_ca_topic_score_gemma":0.0005118433,"domain_scores_codex":[0.9986243,0.0004531761,0.00009716731,0.0002611889,0.0004024373,0.0001617253],"domain_scores_gemma":[0.9951425,0.003389355,0.0004209956,0.0005505274,0.0003452282,0.000151449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001308292,0.00003824327,0.0009687357,0.0001889369,0.0000762538,0.0002318434,0.0004372184,0.1108201,0.004500499,0.8467974,0.001135934,0.03467397],"study_design_scores_gemma":[0.0000216024,0.00007684837,0.0006709137,0.0000375018,0.00003027578,0.0001192732,0.0001907523,0.3285993,0.001300049,0.6669766,0.001948096,0.00002863268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1446184,0.001711681,0.8145389,0.0044868,0.0001563757,0.00007691384,0.0001650916,0.0003774197,0.03386839],"genre_scores_gemma":[0.9644744,0.0005508146,0.0303006,0.0002088867,0.0001458847,0.00007798991,0.0000716709,0.0001316576,0.004038016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004859861,"threshold_uncertainty_score":0.01625788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01190995596260097,"score_gpt":0.2662370247087128,"score_spread":0.2543270687461118,"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."}}