{"id":"W2109028261","doi":"10.1109/ism.2008.28","title":"Spoken Term Detection Using Visual Spectrogram Matching","year":2008,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Spectrogram; Computer science; Term (time); Matching (statistics); Query expansion; Artificial intelligence; Speech recognition; Information retrieval; Query by Example; Pattern recognition (psychology); Web search query; Search engine; Mathematics","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":[0.00008495746,0.00009059989,0.00008608541,0.00008602771,0.0003090626,0.0001484002,0.0002524067,0.00003386593,0.0000155791],"category_scores_gemma":[0.00000637267,0.00008068972,0.00004400845,0.0002949913,0.00002332065,0.0006618577,0.00009766677,0.00009360557,0.00003961417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000463253,"about_ca_system_score_gemma":0.00004153885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000537792,"about_ca_topic_score_gemma":0.00001123419,"domain_scores_codex":[0.9991986,0.00001431152,0.0001200857,0.0002373816,0.0001776371,0.0002520258],"domain_scores_gemma":[0.9996957,0.00001317837,0.00004716906,0.0001544145,0.00002569848,0.00006389491],"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":[0.000006980876,0.00009679171,0.004758056,0.00001506697,0.00001564713,0.0001526041,0.0009013327,0.0001435929,0.4963409,0.0002446633,0.00002200681,0.4973023],"study_design_scores_gemma":[0.0001641039,0.00005205154,0.003954494,0.00001258818,0.000002472353,0.000596903,0.00002663861,0.01977387,0.9739074,0.001232913,0.000100276,0.0001763282],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5184227,0.00002131659,0.4800403,0.00002878732,0.0001047377,0.00002549516,2.268671e-8,0.0002002456,0.00115638],"genre_scores_gemma":[0.8348022,0.000003158441,0.1648023,0.0001400472,0.0001265475,9.400634e-7,1.680432e-7,0.000005760581,0.0001190026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.497126,"threshold_uncertainty_score":0.3290431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02191147013630208,"score_gpt":0.2734250884361102,"score_spread":0.2515136182998082,"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."}}