{"id":"W1935171541","doi":"10.1109/icassp.1997.596083","title":"Accurate keyword spotting using strictly lexical fillers","year":2002,"lang":"en","type":"article","venue":"","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Keyword spotting; Computer science; Vocabulary; Set (abstract data type); Task (project management); Range (aeronautics); Directory; Natural language processing; Speech recognition; Artificial intelligence; Spotting; Linguistics","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":[0.00008264241,0.00009218868,0.00008654756,0.0001155305,0.0001393041,0.0002144765,0.000695562,0.00006190767,0.0003538863],"category_scores_gemma":[0.00006272277,0.00007687553,0.0000411747,0.0005038758,0.00004189272,0.0005940353,0.0002160796,0.0001016891,0.0002276933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003607796,"about_ca_system_score_gemma":0.000009129817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009682418,"about_ca_topic_score_gemma":4.744678e-7,"domain_scores_codex":[0.9991431,0.00001697602,0.00018,0.0002611598,0.0001667851,0.0002319769],"domain_scores_gemma":[0.9993857,0.00005749221,0.00007037281,0.0004113227,0.00002950233,0.00004556697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001955937,0.000123364,0.001608274,0.000009070846,0.00002245551,0.00002188106,0.0004394546,0.000674457,0.003532192,0.5931776,0.02447668,0.3759126],"study_design_scores_gemma":[0.0002201243,0.00003379303,0.00092632,0.000008280199,0.000003050133,0.00001169786,0.0001872917,0.9660493,0.005895385,0.003767853,0.0226429,0.0002540133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.031578,0.0001073173,0.9295235,0.003986211,0.0002242493,0.00009885131,4.465546e-7,0.001513284,0.03296816],"genre_scores_gemma":[0.9168459,0.00002328964,0.08058649,0.0002984648,0.00002234812,0.000003124346,2.989154e-7,0.000004226765,0.002215932],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9653748,"threshold_uncertainty_score":0.3874807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09808095789106017,"score_gpt":0.2754248937137858,"score_spread":0.1773439358227256,"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."}}