{"id":"W2134443264","doi":"10.1109/icpr.1988.28252","title":"Planning, neural networks and Markov models for automatic speech recognition","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal; McGill University","funders":"","keywords":"Hidden Markov model; Speech recognition; Feature (linguistics); Computer science; Markov model; Markov process; Artificial neural network; Markov chain; Artificial intelligence; Dynamic programming; Pattern recognition (psychology); Natural language processing; Machine learning; Algorithm; Mathematics; Linguistics; Statistics","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":[0.0006111297,0.0006287933,0.0004723366,0.0005414284,0.0003290655,0.00105694,0.0007259705,0.0008120667,0.00376914],"category_scores_gemma":[0.002613704,0.0004553311,0.0005411255,0.0009552212,0.0009447177,0.001776275,0.0005619089,0.001201303,0.0005409672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001295817,"about_ca_system_score_gemma":0.0009058383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009796648,"about_ca_topic_score_gemma":0.01099247,"domain_scores_codex":[0.9996941,0.0001268869,0.00001798348,0.00006433835,0.00006911232,0.00002755216],"domain_scores_gemma":[0.999132,0.0006790157,0.00006623745,0.00004837797,0.00005343236,0.00002093372],"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":[0.0001003301,0.00003108365,0.0003913902,0.0001792286,0.00006814632,0.0001006928,0.0000729139,0.7021813,0.001293836,0.1750454,0.004500439,0.1160353],"study_design_scores_gemma":[0.000006896819,0.00001042004,0.0001061216,0.00001768648,0.000008706847,0.00002284537,0.00001034869,0.8158095,0.0004603456,0.1808356,0.00269994,0.00001155162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007525534,0.005778298,0.9783775,0.001013574,0.0001634048,0.00002850068,0.0001992681,0.000924808,0.005989006],"genre_scores_gemma":[0.525733,0.01219741,0.4367247,0.0004334225,0.0005238723,0.0004018534,0.0008873655,0.0003316523,0.02276678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009796648,"threshold_uncertainty_score":0.01947927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03772102363430958,"score_gpt":0.2638910836678187,"score_spread":0.2261700600335091,"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."}}