{"id":"W2148812765","doi":"10.1006/csla.1999.0136","title":"A path-stack algorithm for optimizing dynamic regimes in a statistical hidden dynamic model of speech","year":2000,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Hidden Markov model; Utterance; Speech recognition; Stack (abstract data type); Path (computing); Phone; Reduction (mathematics); Set (abstract data type); Segmentation; Algorithm; Artificial intelligence; Pattern recognition (psychology)","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":[0.001977727,0.001675129,0.001687768,0.001257427,0.001048162,0.0009459764,0.001802694,0.002524084,0.006168039],"category_scores_gemma":[0.004205304,0.001857447,0.00131408,0.001219117,0.001102629,0.002129689,0.001900424,0.002398839,0.001133612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001164107,"about_ca_system_score_gemma":0.003090504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01753192,"about_ca_topic_score_gemma":0.0179488,"domain_scores_codex":[0.9996378,0.0001224149,0.00002253748,0.00009269724,0.00007798621,0.00004657844],"domain_scores_gemma":[0.9989518,0.0007883896,0.00004380521,0.00005734916,0.0001164475,0.00004219449],"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":[0.0001422329,0.00007141579,0.0003617383,0.00005080584,0.00007492465,0.0000523985,0.0000738671,0.833923,0.001610419,0.01350099,0.001583041,0.1485551],"study_design_scores_gemma":[0.00001160138,0.00001384458,0.00002392665,0.000003261955,0.000006643299,0.0000042506,0.000004089014,0.9961478,0.0002234829,0.003331625,0.0002250147,0.00000455433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005868726,0.0001124156,0.9923491,0.00006651079,0.0000184479,0.00005023884,0.00004589291,0.0008689514,0.0006195935],"genre_scores_gemma":[0.1247015,0.000196856,0.871011,0.0001006435,0.00003635781,0.0004244341,0.0003638718,0.0005831082,0.002582128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01753192,"threshold_uncertainty_score":0.03485978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01325843623408658,"score_gpt":0.2679317228246664,"score_spread":0.2546732865905798,"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."}}