{"id":"W3099527391","doi":"10.3389/fncom.2020.573554","title":"Hierarchical Sequencing and Feedforward and Feedback Control Mechanisms in Speech Production: A Preliminary Approach for Modeling Normal and Disordered Speech","year":2020,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"RWTH Aachen University","keywords":"Utterance; Speech production; Computer science; Production (economics); Feed forward; Speech recognition; Hierarchy; Selection (genetic algorithm); Artificial intelligence","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":[0.0003202347,0.0001858345,0.0002666234,0.0001683643,0.0002078474,0.00008362859,0.0001874026,0.00007478264,4.744252e-7],"category_scores_gemma":[0.0008379766,0.0001811348,0.00002329443,0.0003619422,0.0004066999,0.0003254015,0.0001311423,0.0003112548,1.369129e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002158647,"about_ca_system_score_gemma":0.0000739123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000714228,"about_ca_topic_score_gemma":0.000001482944,"domain_scores_codex":[0.9979733,0.0001487334,0.0003055942,0.001008543,0.0002299414,0.0003339014],"domain_scores_gemma":[0.9994997,0.0001908917,0.00007218451,0.00008364181,0.00002669003,0.000126843],"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":[0.002217508,0.0003293325,0.01007063,0.0005130045,0.000007046566,0.0008017735,0.00658133,0.6320102,0.323255,0.002071724,0.0002063783,0.02193611],"study_design_scores_gemma":[0.00113276,0.0003246845,0.0008027407,0.0000155322,0.000006032863,0.0005831756,0.0003376495,0.9874865,0.001615544,0.007489832,0.00001017856,0.0001954125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7159185,0.00009808478,0.2807142,0.002322481,0.0002338769,0.0006399603,0.00002351279,0.00003262408,0.00001681226],"genre_scores_gemma":[0.9197791,0.00003291715,0.07755681,0.002518227,0.00005652104,0.0000272902,0.000004956384,0.00001342431,0.00001081822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3554763,"threshold_uncertainty_score":0.7386462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03401506892624687,"score_gpt":0.2546217042356806,"score_spread":0.2206066353094338,"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."}}