{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001011116,0.0009129151,0.0008160166,0.001377035,0.0003608314,0.0006888775,0.0009239003,0.0006901093,0.00440447],"category_scores_gemma":[0.002788754,0.0003021069,0.0007372227,0.001584208,0.0004267759,0.0008319113,0.0006705517,0.001143218,0.00487347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005273696,"about_ca_system_score_gemma":0.0004553847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001433298,"about_ca_topic_score_gemma":0.001485174,"domain_scores_codex":[0.9988042,0.0003604802,0.00005936846,0.0002367401,0.0004881312,0.00005103348],"domain_scores_gemma":[0.9990479,0.0003745365,0.00007781311,0.0001581051,0.0003125395,0.00002916209],"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":[0.0002140983,0.00008680468,0.0005854014,0.000292484,0.00009499452,0.0001399395,0.00007511274,0.02998503,0.03945452,0.01413861,0.008867421,0.9060656],"study_design_scores_gemma":[0.00004655541,0.000203067,0.002298689,0.00007026004,0.0000629992,0.0005307686,0.00005672808,0.8942524,0.03002012,0.01872877,0.0536046,0.0001250442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005133111,0.002236672,0.9892992,0.0001873796,0.0002112103,0.00004905542,0.000171161,0.001149738,0.001562532],"genre_scores_gemma":[0.1141152,0.002303696,0.873432,0.0001831035,0.0002994474,0.0002782471,0.0008914483,0.0002871141,0.008209626],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00440447,"threshold_uncertainty_score":0.01473439,"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."}}