{"id":"W2145334935","doi":"10.1080/1206212x.2007.11441842","title":"Incorporating Phonetic Knowledge Into an Evolutionary Subspace Approach for Robust Speech Recognition","year":2007,"lang":"en","type":"article","venue":"International Journal of Computers and Applications","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal; Université de Moncton","funders":"","keywords":"Computer science; TIMIT; Speech recognition; Robustness (evolution); Subspace topology; Word error rate; Noise (video); Cepstrum; Mel-frequency cepstrum; Range (aeronautics); Background noise; Artificial intelligence; Pattern recognition (psychology); Hidden Markov model; Feature extraction","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":[0.000688567,0.0005873104,0.0007822597,0.0007026527,0.000359294,0.0005867506,0.0006675164,0.000683265,0.001185901],"category_scores_gemma":[0.001512192,0.0003427756,0.0006106185,0.0006280066,0.0004174055,0.0007226153,0.0006459872,0.0005874551,0.0005392396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002717569,"about_ca_system_score_gemma":0.0005351923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002262113,"about_ca_topic_score_gemma":0.002491051,"domain_scores_codex":[0.9996257,0.0001114081,0.00002541841,0.00008615929,0.0001213909,0.00002994684],"domain_scores_gemma":[0.9996444,0.0001560503,0.00003190789,0.00005118563,0.0001025912,0.00001386395],"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":[0.00005040566,0.00007206498,0.0007155932,0.00006496189,0.00008857824,0.00009743057,0.0001563917,0.4805208,0.01775645,0.0125003,0.0005106853,0.4874664],"study_design_scores_gemma":[0.000004154759,0.00003470658,0.0002089027,0.000004104044,0.00001124691,0.00004988272,0.00001286863,0.9939068,0.001671004,0.003252098,0.0008327808,0.00001137471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005920806,0.00009295475,0.9932053,0.00002991,0.000009512812,0.00001276432,0.000008182408,0.0002278466,0.0004927321],"genre_scores_gemma":[0.2004645,0.0002955832,0.7964208,0.0000741725,0.00004166489,0.0001213994,0.0001305275,0.00009804413,0.00235334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002262113,"threshold_uncertainty_score":0.004497886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02699950347830216,"score_gpt":0.2936628017158363,"score_spread":0.2666632982375342,"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."}}