{"id":"W1545111201","doi":"10.5281/zenodo.37758","title":"A Hybrid Hmm/Autoregressive Time-Delay Neural Network Automatic Speech Recognition System","year":2002,"lang":"en","type":"article","venue":"INFM-OAR (INFN Catania)","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Computer science; Hidden Markov model; Speech recognition; Autoregressive model; Artificial neural network; Time delay neural network; Artificial intelligence; Backpropagation; Pattern recognition (psychology); Set (abstract data type); Speech processing; Task (project management); Engineering","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":[0.0006187712,0.000421283,0.000968925,0.00050506,0.0003975622,0.00067136,0.001008486,0.0008183647,0.006838008],"category_scores_gemma":[0.0004827036,0.0003986353,0.0004251648,0.0004366872,0.0001527324,0.0007355774,0.0006180854,0.0005205877,0.005226603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003680502,"about_ca_system_score_gemma":0.0006895593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005800343,"about_ca_topic_score_gemma":0.009400979,"domain_scores_codex":[0.9997229,0.00003309572,0.00002099381,0.0001018797,0.00007579379,0.00004539081],"domain_scores_gemma":[0.9997284,0.00006362494,0.00001313295,0.00003991721,0.0001251399,0.0000298015],"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":[0.00114783,0.0005069016,0.00193473,0.0002129414,0.0002018567,0.0002580314,0.00007430737,0.01637524,0.1809907,0.0009963273,0.01084787,0.7864533],"study_design_scores_gemma":[0.0002399016,0.0006569453,0.009324933,0.00006435299,0.0005045604,0.0005865951,0.00005256477,0.8824933,0.08680021,0.001407751,0.01775943,0.000109432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1013122,0.002115747,0.8549779,0.0003795383,0.0009664649,0.0003218861,0.001806264,0.02890566,0.009214355],"genre_scores_gemma":[0.5305138,0.0008271027,0.4296359,0.0005041622,0.00021864,0.0004002676,0.003447208,0.0003947669,0.03405821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006838008,"threshold_uncertainty_score":0.02287537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01872088569544155,"score_gpt":0.2136637995327042,"score_spread":0.1949429138372626,"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."}}