{"id":"W2125185939","doi":"10.1109/ccece.2007.400","title":"Modified Linear Discriminant Analysis for Speech Recognition","year":2007,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Linear discriminant analysis; Artificial intelligence; Pattern recognition (psychology); Discriminant; Computer science; Speech recognition; Cluster analysis; Hidden Markov model; Kernel Fisher discriminant analysis; Generalization; Mathematics; Facial recognition system","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":[0.0004801201,0.00007745049,0.0001219911,0.0002102174,0.000105746,0.00008929895,0.0002732426,0.00003950372,0.00001492376],"category_scores_gemma":[0.00004871619,0.00006111817,0.0001161423,0.0007736451,0.0000125958,0.0003482093,0.0000467246,0.00004186001,0.00002572229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001833484,"about_ca_system_score_gemma":0.0000202486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002366025,"about_ca_topic_score_gemma":0.00007805986,"domain_scores_codex":[0.9991498,0.000006425116,0.000178779,0.000266012,0.0001408221,0.0002581786],"domain_scores_gemma":[0.9994727,0.00007339465,0.0000567381,0.000217243,0.0001067919,0.0000731678],"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":[0.00001570284,0.00005675497,0.0004393715,0.00001361805,0.00007012184,0.000009340077,0.0001325054,0.00006417176,0.006200051,0.0005605865,0.000253627,0.9921842],"study_design_scores_gemma":[0.0003348677,0.00006428947,0.002949912,0.000008732149,0.0001027133,0.000006772569,0.00006472679,0.06088896,0.9248193,0.009789481,0.0007465261,0.0002237361],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06807137,0.00002208705,0.9263898,0.0003402576,0.00009537958,0.00008790005,0.000001572844,0.0001215247,0.00487011],"genre_scores_gemma":[0.4458187,0.000002335601,0.553251,0.0002756372,0.00007319599,0.000003670349,0.000009761467,0.000003312605,0.0005624733],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9919604,"threshold_uncertainty_score":0.2492327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06008387189968824,"score_gpt":0.3061398985061713,"score_spread":0.2460560266064831,"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."}}