{"id":"W2991975083","doi":"","title":"Strategies to Enhance Whispered Speech Speaker Verification: A Comparative Analysis","year":2015,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Speech recognition; Noise (video); Task (project management); Feature (linguistics); Speech enhancement; Speech technology; Speech processing; Background noise; Artificial intelligence; Linguistics; Engineering; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.00374284,0.0007315184,0.0006321023,0.001155228,0.0003764858,0.000739747,0.0006109087,0.0006526666,0.002312645],"category_scores_gemma":[0.006848709,0.0001744869,0.0006044098,0.0004381372,0.0003026261,0.001191025,0.0008881531,0.0003671224,0.0009177611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003300624,"about_ca_system_score_gemma":0.0005436619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001506774,"about_ca_topic_score_gemma":0.002280293,"domain_scores_codex":[0.997733,0.000992597,0.0001546681,0.0002775048,0.0006994103,0.0001428369],"domain_scores_gemma":[0.9962037,0.002316894,0.0001177712,0.0003143289,0.0009748194,0.00007242636],"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":[0.001432114,0.0003051229,0.006326011,0.000876132,0.0002973075,0.0002649569,0.0006437284,0.007182861,0.1002379,0.001568134,0.0006603498,0.8802054],"study_design_scores_gemma":[0.0002728256,0.012597,0.0893243,0.0004026913,0.002967843,0.005973423,0.003170607,0.2845266,0.5684106,0.003136344,0.02890159,0.0003162301],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7368091,0.01582961,0.2330585,0.000277929,0.0001980183,0.0004460012,0.000224894,0.001115375,0.01204063],"genre_scores_gemma":[0.8973919,0.003315918,0.09605113,0.0000748462,0.0000667823,0.00008372463,0.0004485641,0.00007886638,0.002488413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00374284,"threshold_uncertainty_score":0.01979429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05715611910217276,"score_gpt":0.3120466947824186,"score_spread":0.2548905756802459,"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."}}