{"id":"W2501000458","doi":"10.1109/ijcnn.1991.155435","title":"Global optimization of a neural network-hidden Markov model hybrid","year":2002,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Hidden Markov model; TIMIT; Artificial neural network; Computer science; Speech recognition; Artificial intelligence; Sequence (biology); Markov model; Pattern recognition (psychology); SIGNAL (programming language); Markov chain; Machine learning","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":[0.00008032969,0.00007436934,0.0001044066,0.00002732115,0.00003903898,0.00005004307,0.0003354157,0.00002425004,0.0005825331],"category_scores_gemma":[0.00001965216,0.00006647834,0.00005918591,0.0002516911,0.00001839986,0.0002661838,0.00008394266,0.00002488545,0.00003182015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001742417,"about_ca_system_score_gemma":0.000008261105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008577529,"about_ca_topic_score_gemma":0.000003675728,"domain_scores_codex":[0.9992823,0.00003191167,0.0001663929,0.0001796159,0.0001743682,0.0001654023],"domain_scores_gemma":[0.999558,0.00002729222,0.00005284797,0.000237554,0.0000641325,0.00006014476],"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":[0.000004050373,0.00007303842,0.0003336052,0.000004380638,0.00001275436,0.000007578266,0.00002651239,0.3639968,0.000005189112,0.004553694,0.02744502,0.6035373],"study_design_scores_gemma":[0.0001232886,0.00001481088,0.00009115171,0.000004038297,0.000003479921,0.0000202379,0.000002116602,0.9985353,0.00009729505,0.0009968542,0.00003302466,0.00007842855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002654519,0.00003907341,0.921217,0.0005280453,0.00009989617,0.00006392239,0.000003041973,0.0001183211,0.07527623],"genre_scores_gemma":[0.361871,0.00001979502,0.6368325,0.0005063468,0.00003021832,0.000003280813,0.000001116022,0.000002951736,0.0007327614],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6345385,"threshold_uncertainty_score":0.6378329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0267549274776513,"score_gpt":0.2269273477419959,"score_spread":0.2001724202643446,"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."}}