{"id":"W2170389424","doi":"10.1109/tsp.2007.896118","title":"Design and Performance Analysis of Bayesian, Neyman–Pearson, and Competitive Neyman–Pearson Voice Activity Detectors","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Bayesian probability; Detector; Pearson product-moment correlation coefficient; Computer science; False alarm; Detection theory; Speech recognition; Artificial intelligence; Pattern recognition (psychology); Statistics; Mathematics; Telecommunications","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":[0.01109555,0.00135972,0.001472742,0.001279319,0.0007299919,0.002157694,0.002924337,0.002362317,0.001856323],"category_scores_gemma":[0.02916117,0.0009285613,0.00068478,0.0008412015,0.001234531,0.002089289,0.001705049,0.001167477,0.0007692266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002007282,"about_ca_system_score_gemma":0.00237014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001847484,"about_ca_topic_score_gemma":0.00144375,"domain_scores_codex":[0.9927363,0.00397597,0.0002516937,0.0009784222,0.001713203,0.0003443179],"domain_scores_gemma":[0.9844586,0.0111516,0.0008283052,0.0007441184,0.002504244,0.0003131289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001744359,0.000328099,0.006222542,0.0004282296,0.0004347133,0.0002324918,0.0002712408,0.4445105,0.01758679,0.0942114,0.002318067,0.4317116],"study_design_scores_gemma":[0.00004760254,0.0002843897,0.000603504,0.00001444716,0.00004555337,0.0002418575,0.00001932375,0.9853117,0.005387076,0.006628647,0.001376811,0.00003909026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007661629,0.0005017508,0.9904061,0.00007740436,0.00002200716,0.00008048941,0.00002622571,0.0001909349,0.001033533],"genre_scores_gemma":[0.4571726,0.0006374626,0.5392064,0.0002509032,0.00009549902,0.0004499807,0.0001990976,0.00006830235,0.001919839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01109555,"threshold_uncertainty_score":0.05867958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01765592319943949,"score_gpt":0.2495717309958363,"score_spread":0.2319158077963969,"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."}}