{"id":"W1890614125","doi":"10.1109/wescan.1991.160549","title":"A speech recognition system using a neural network model for vocal shaping","year":2002,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"","keywords":"Speech recognition; Computer science; Artificial neural network; Utterance; Formant; Linear predictive coding; Coding (social sciences); Vowel; Speaker recognition; Artificial intelligence; Backpropagation; Time delay neural network; Pattern recognition (psychology); Software; Vocabulary; Feature extraction; Speech coding; Mathematics","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":[0.0001862619,0.0000987657,0.0001243981,0.00003691404,0.0002441827,0.0002183197,0.0002412667,0.00004734885,0.0000114376],"category_scores_gemma":[0.00000983668,0.00008877247,0.000059153,0.0002067904,0.00001257993,0.0005667285,0.00007878129,0.00006252598,0.00001476071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004515926,"about_ca_system_score_gemma":0.00001607765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006846956,"about_ca_topic_score_gemma":0.000002478455,"domain_scores_codex":[0.9990693,0.00001716178,0.0001871619,0.0002838192,0.0001312113,0.0003113702],"domain_scores_gemma":[0.9996103,0.00003419274,0.00007259382,0.0001569068,0.00006770276,0.00005828048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006333236,0.00003209429,0.00002327984,0.0002167211,0.00001341722,0.00001266394,0.0006478248,0.1350738,0.00044609,0.003936627,0.005422027,0.8541691],"study_design_scores_gemma":[0.0001767938,0.00001285236,0.000001808179,0.0001160621,0.000006547801,0.00005930702,0.00001682622,0.9978787,0.0001896254,0.001357458,0.00005113871,0.0001329255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02377016,0.000101611,0.9725451,0.0002914338,0.0002399337,0.0001547794,7.084593e-7,0.000247245,0.002649022],"genre_scores_gemma":[0.5627016,7.459618e-7,0.4359107,0.0009237662,0.0002599302,0.000007989848,5.926557e-7,0.00000730799,0.0001874195],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8628049,"threshold_uncertainty_score":0.3620036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1962968641168446,"score_gpt":0.2772317362186711,"score_spread":0.08093487210182651,"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."}}