{"id":"W2405082712","doi":"10.21437/interspeech.2012-171","title":"A correlational discriminant approach to feature extraction for robust speech recognition","year":2012,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Linear discriminant analysis; Pattern recognition (psychology); Artificial intelligence; Euclidean distance; Speech recognition; Feature vector; Noise (video); Dimensionality reduction; Feature extraction; Discriminant; Mathematics; Computer science; Word error rate; Locality; Feature (linguistics); Correlation; Image (mathematics)","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":[0.001017298,0.000737012,0.0008499285,0.001057156,0.0005032552,0.0007858272,0.0007679866,0.0004959523,0.002523995],"category_scores_gemma":[0.002786323,0.0003382631,0.0006966664,0.001368511,0.0005718752,0.0007425885,0.0006870698,0.001143273,0.001641402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004927471,"about_ca_system_score_gemma":0.0008806969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001225912,"about_ca_topic_score_gemma":0.001668483,"domain_scores_codex":[0.9992488,0.0001905442,0.00004552037,0.0001408427,0.0003329476,0.00004133565],"domain_scores_gemma":[0.9989152,0.0003683482,0.00009886066,0.0001538268,0.0004338692,0.00002985588],"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":[0.0001679311,0.0002123115,0.0009489568,0.0002911387,0.0001001503,0.0001874817,0.0001147381,0.06046797,0.1164314,0.03806664,0.004609411,0.7784019],"study_design_scores_gemma":[0.00001974034,0.0002219442,0.001719527,0.00002965654,0.00004262308,0.0003348114,0.00003960533,0.9287798,0.04312038,0.01046352,0.01515177,0.00007665542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002713876,0.0001948479,0.9961659,0.00006740753,0.00003210488,0.00003392129,0.00004848238,0.0002294989,0.0005139488],"genre_scores_gemma":[0.09413972,0.000556396,0.9018983,0.0000698286,0.0001135638,0.0001926118,0.0002557851,0.0001115838,0.002662273],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002523995,"threshold_uncertainty_score":0.008443594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06804125987193632,"score_gpt":0.2841385356375415,"score_spread":0.2160972757656052,"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."}}