{"id":"W2005604008","doi":"10.1109/isie.2006.295643","title":"Incorporation of State-Level Variable Stime-Varying Property into the HMM","year":2006,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Hidden Markov model; Property (philosophy); TIMIT; Computer science; Process (computing); Speech recognition; Variable (mathematics); State (computer science); Task (project management); Pattern recognition (psychology); Artificial intelligence; State variable; Mathematics; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003461493,0.00006858012,0.00008644652,0.0000519526,0.0001030275,0.00008953704,0.0003534963,0.00002559082,0.0001415286],"category_scores_gemma":[0.00004015102,0.00003417741,0.00002659097,0.0003216023,0.00003298634,0.0003497724,0.00008837178,0.00004756028,0.00006577772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001980134,"about_ca_system_score_gemma":0.00007717286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001851216,"about_ca_topic_score_gemma":0.0001181191,"domain_scores_codex":[0.9992583,0.00005987175,0.0002081097,0.0001605174,0.0002055936,0.0001075594],"domain_scores_gemma":[0.9993948,0.00009431149,0.00008467416,0.0002713014,0.000133118,0.00002181413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001891155,0.0003095978,0.001150883,0.00003818193,0.00003313276,0.000006143032,0.0009861026,0.0008036416,0.05642924,0.1135968,0.01529354,0.8113338],"study_design_scores_gemma":[0.0005601295,0.0001031728,0.004050807,0.00004921876,0.00001400606,0.00001607231,0.0000907297,0.3731629,0.4316792,0.1761906,0.01369604,0.0003870602],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00535328,0.00001294138,0.9133563,0.0008793671,0.00009214859,0.0001421801,0.000001478512,0.00008800463,0.08007431],"genre_scores_gemma":[0.5066338,0.000002842513,0.4846825,0.0003364482,0.00003170157,0.00001488594,0.000003101232,0.000005298717,0.00828944],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8109468,"threshold_uncertainty_score":0.2798496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02966195465900871,"score_gpt":0.2224253482776512,"score_spread":0.1927633936186425,"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."}}