{"id":"W2126203737","doi":"10.1109/asru.2009.5372931","title":"Unsupervised spoken keyword spotting via segmental DTW on Gaussian posteriorgrams","year":2009,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":338,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Keyword spotting; Computer science; Dynamic time warping; Speech recognition; Artificial intelligence; TIMIT; Ranking (information retrieval); Gaussian; Spotting; Viterbi algorithm; Gaussian process; Natural language processing; Pattern recognition (psychology); Hidden Markov model","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":[0.0007930902,0.000869933,0.0009063541,0.001152565,0.0002704611,0.0005481774,0.0009579262,0.0006482904,0.001581764],"category_scores_gemma":[0.002860628,0.0003096218,0.0005206192,0.001234975,0.0005056833,0.001230209,0.000911046,0.0008420949,0.001410083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003898407,"about_ca_system_score_gemma":0.0007091792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003312098,"about_ca_topic_score_gemma":0.004594367,"domain_scores_codex":[0.9993517,0.0001734324,0.00003425038,0.0002155748,0.0001560872,0.00006910531],"domain_scores_gemma":[0.9988548,0.000572736,0.0001275996,0.000130417,0.0002592369,0.00005525097],"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":[0.0006080598,0.0001162296,0.001384608,0.000154055,0.00008709407,0.000203291,0.0001801532,0.06335077,0.09154779,0.003853808,0.003340172,0.835174],"study_design_scores_gemma":[0.00001985818,0.0001036105,0.001983478,0.00001037424,0.0000261141,0.0001646178,0.00005002174,0.9626455,0.02829366,0.004683563,0.001994618,0.00002458814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02944787,0.0002216629,0.9675098,0.0000595223,0.00002662997,0.00003578912,0.000160597,0.001860084,0.0006780208],"genre_scores_gemma":[0.4482489,0.00040559,0.5417877,0.0001459889,0.0001288703,0.0001637991,0.001723008,0.0007943782,0.006601762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003312098,"threshold_uncertainty_score":0.006585658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01218157054232683,"score_gpt":0.2340915820983202,"score_spread":0.2219100115559933,"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."}}