{"id":"W2100555417","doi":"10.1109/tsa.2003.815518","title":"A soft voice activity detector based on a laplacian-gaussian model","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":141,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Speech recognition; Hidden Markov model; Computer science; Noise (video); Discrete cosine transform; Gaussian; Posterior probability; Detector; Probability distribution; Bayesian probability; Pattern recognition (psychology); Mathematics; Artificial intelligence; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003562593,0.0004161449,0.0003410943,0.0003604026,0.0009684105,0.000606284,0.0003944181,0.0001811622,0.00001700041],"category_scores_gemma":[0.00003084129,0.000384641,0.0001285001,0.0008248934,0.0001023885,0.001065649,0.000003197423,0.0005776856,0.00004160837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001086623,"about_ca_system_score_gemma":0.0004745297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000643068,"about_ca_topic_score_gemma":0.00002792386,"domain_scores_codex":[0.9975868,0.0000905393,0.0002765445,0.0008859347,0.0005015883,0.000658521],"domain_scores_gemma":[0.9988195,0.000116679,0.0001641525,0.0004837003,0.0001037627,0.000312175],"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":[0.0001052111,0.000442681,0.0000225077,0.0001565473,0.00001928927,0.00004043981,0.0003032471,0.02477002,0.0367275,0.00002597224,0.00004330066,0.9373433],"study_design_scores_gemma":[0.0008085436,0.0001765064,0.00002471129,0.0002125749,0.00002254617,0.00005016376,0.00002105662,0.3254013,0.6719838,0.0005836834,0.0002773643,0.0004376633],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03208615,0.00009934748,0.9636335,0.0006842148,0.0002040632,0.0002058487,0.000008055456,0.0004141613,0.002664625],"genre_scores_gemma":[0.8911645,0.00001212378,0.1071054,0.001164386,0.00003347229,0.00003880147,3.377499e-7,0.00003498477,0.0004459763],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9369056,"threshold_uncertainty_score":0.9998605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01821155687566045,"score_gpt":0.2488104677728221,"score_spread":0.2305989108971617,"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."}}