{"id":"W2752215935","doi":"10.1007/978-3-319-67020-1","title":"Canonical Correlation Analysis in Speech Enhancement","year":2017,"lang":"en","type":"book","venue":"Springer briefs in electrical and computer engineering","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Canonical correlation; Correlation; Speech recognition; Computer science; Mathematics; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003056398,0.0003525371,0.000649658,0.0009175627,0.00006429104,0.0003524802,0.0007826392,0.000281864,0.000005491117],"category_scores_gemma":[0.00002714186,0.0003761861,0.0001295392,0.0006034512,0.0000246646,0.0002822776,0.000412253,0.0008439085,0.000008510915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003844874,"about_ca_system_score_gemma":0.000219165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004587185,"about_ca_topic_score_gemma":0.00005665411,"domain_scores_codex":[0.9977993,0.00001955995,0.0004861605,0.0007927726,0.0003463198,0.0005559357],"domain_scores_gemma":[0.9990522,0.0001088387,0.0001569939,0.0005157951,0.00003727005,0.000128866],"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":[0.00002046629,0.0001919643,0.007055953,0.0002418504,0.0004701598,0.0009354109,0.0003685352,0.03468443,0.0002047882,0.009749498,0.0008066749,0.9452702],"study_design_scores_gemma":[0.0004605072,0.00008084088,0.01687489,0.0003285035,0.00005798233,0.00002506425,1.105191e-7,0.9702678,0.0007040752,0.001323014,0.009197957,0.0006792005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003068211,0.00188658,0.9891942,0.0001088866,0.0003887191,0.0001912511,6.932381e-7,0.0001143541,0.005047159],"genre_scores_gemma":[0.3762907,0.001860611,0.5251664,0.001031277,0.003012899,0.0001534021,0.00009043948,0.0002423311,0.0921519],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.944591,"threshold_uncertainty_score":0.999869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006270466538789298,"score_gpt":0.2114101561144959,"score_spread":0.2051396895757066,"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."}}