{"id":"W2115900223","doi":"10.1109/icassp.1994.389284","title":"Correcting complex false starts in spontaneous speech","year":2002,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Utterance; Computer science; Speech recognition; Word (group theory); Identification (biology); Artificial intelligence; Natural language processing; Linguistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003217854,0.000886216,0.001007487,0.0009012604,0.0006439992,0.001871917,0.0009766011,0.001355506,0.002681937],"category_scores_gemma":[0.0371989,0.0004844769,0.0004103063,0.0006191815,0.0008910741,0.001791497,0.001591931,0.001070178,0.001745565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003837417,"about_ca_system_score_gemma":0.0005356404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001286688,"about_ca_topic_score_gemma":0.001655656,"domain_scores_codex":[0.9944732,0.00183963,0.000453612,0.001209529,0.001732292,0.0002917777],"domain_scores_gemma":[0.9527111,0.02961378,0.003694244,0.008699154,0.004848734,0.00043294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005744275,0.000294505,0.03770448,0.0009039809,0.000257039,0.01039698,0.005653682,0.03268062,0.2540589,0.009780832,0.008387046,0.6341377],"study_design_scores_gemma":[0.0002374143,0.001740857,0.06462639,0.0003474147,0.0003959005,0.01659935,0.00261611,0.3291459,0.5299037,0.0235309,0.03040367,0.000452523],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5750719,0.0009667352,0.4123604,0.0002778468,0.0003982654,0.000139003,0.0008615426,0.004978088,0.004946166],"genre_scores_gemma":[0.9004996,0.0002539027,0.09313688,0.0001264546,0.0001039091,0.00007042519,0.001445987,0.0008859067,0.003476866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003217854,"threshold_uncertainty_score":0.0170179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0633903589158387,"score_gpt":0.244796490937937,"score_spread":0.1814061320220983,"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."}}