{"id":"W1970996882","doi":"10.1121/1.1420380","title":"An overlapping-feature-based phonological model incorporating linguistic constraints: Applications to speech recognition","year":2002,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Hidden Markov model; Feature (linguistics); Syllable; Speech recognition; Phrase; Artificial intelligence; Morpheme; Word (group theory); Natural language processing; Dependency (UML); Acoustic model; Linguistics; Speech processing","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":[0.0006287445,0.0001370342,0.0002654438,0.00003619264,0.0002586505,0.00005832083,0.001196171,0.00008771928,0.00007348401],"category_scores_gemma":[0.0006184918,0.00007961867,0.0002820008,0.0005287021,0.0005081889,0.0001191617,0.0001085919,0.0004416531,0.00002270242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006746544,"about_ca_system_score_gemma":0.00008146173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004965179,"about_ca_topic_score_gemma":2.382048e-7,"domain_scores_codex":[0.9985454,0.0001891095,0.0004207758,0.0001573336,0.0004769884,0.0002103708],"domain_scores_gemma":[0.9977703,0.0006924653,0.0005030176,0.0004353143,0.0004400764,0.0001588188],"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":[0.00003552953,0.0005676827,0.00001979004,0.00002328566,0.0000817388,0.000002779886,0.0006827074,0.01021529,0.02291376,0.0001048981,0.01048435,0.9548682],"study_design_scores_gemma":[0.000214703,0.0002031206,0.00005144358,0.00005798311,0.00007410106,0.00006925576,0.0002613244,0.9887065,0.002865531,0.007024839,0.0003485112,0.0001226782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001539406,0.00004178582,0.9904214,0.007114032,0.0001031881,0.0001923135,0.00001354221,0.00003475283,0.000539583],"genre_scores_gemma":[0.3654465,0.00002472999,0.6302845,0.00407563,0.0001445785,0.000004470513,4.646409e-7,0.00000627895,0.00001274002],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9784912,"threshold_uncertainty_score":0.3246755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04045392051542798,"score_gpt":0.2698564663430641,"score_spread":0.2294025458276361,"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."}}