{"id":"W7018737232","doi":"","title":"Discriminative manifold learning for automatic speech recognition","year":2016,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Discriminative model; Pattern recognition (psychology); Nonlinear dimensionality reduction; Manifold alignment; Feature vector; Feature (linguistics); Manifold (fluid mechanics); Linear discriminant analysis; Curse of dimensionality","routes":{"ca_aff":true,"ca_fund":true,"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","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009327498,0.0007797559,0.0007088165,0.0006007256,0.001588569,0.0002784631,0.001168012,0.0007864689,0.0002445764],"category_scores_gemma":[0.001124336,0.0006983218,0.0004791156,0.0004574544,0.00002906205,0.002504186,0.0001511281,0.001031889,0.001488195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006142127,"about_ca_system_score_gemma":0.00006648061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006239032,"about_ca_topic_score_gemma":0.0001538956,"domain_scores_codex":[0.9955173,0.0004184128,0.0009014902,0.001470764,0.0008418766,0.0008501979],"domain_scores_gemma":[0.9966419,0.0006631921,0.0009140671,0.0006949234,0.000787023,0.0002988366],"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":[0.00006303364,0.0001255865,0.000001743893,0.0004835219,0.00009825952,0.0000304174,0.00002260899,5.306135e-7,0.06561337,0.01671968,0.00004604577,0.9167952],"study_design_scores_gemma":[0.002119021,0.0006324108,0.0003558733,0.00428537,0.0002867586,0.00005816418,0.0007618404,0.0009124191,0.6681505,0.2985171,0.02163566,0.002284856],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7562307,0.0002468068,0.0006273044,0.0002013251,0.007046308,0.004234306,0.001614937,0.002313391,0.2274849],"genre_scores_gemma":[0.8704849,0.0005027249,0.07034314,0.0005780613,0.0002920339,0.002193193,0.005428202,0.0004751316,0.04970263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9145104,"threshold_uncertainty_score":0.9997112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02641135575066583,"score_gpt":0.2613660615640314,"score_spread":0.2349547058133655,"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."}}