{"id":"W2978471304","doi":"10.1109/ism.workshops.2007.47","title":"Evaluation of Speech Enhancement Techniques for Speaker Identification in Noisy Environments","year":2007,"lang":"en","type":"article","venue":"Ninth IEEE International Symposium on Multimedia Workshops (ISMW 2007)","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Speech recognition; Computer science; TIMIT; Speech enhancement; Speaker identification; Noise (video); Speaker recognition; Identification (biology); Background noise; Noise measurement; Speech processing; Voice activity detection; SIGNAL (programming language); Linear predictive coding; Artificial intelligence; Hidden Markov model; Noise reduction; Telecommunications","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":[0.002879272,0.001136739,0.0008341506,0.0008134533,0.000293898,0.0004789369,0.0006171842,0.0008597514,0.001097065],"category_scores_gemma":[0.007608068,0.0003087081,0.0004993808,0.0004651803,0.0002732872,0.0008070255,0.0005394678,0.0003179031,0.0004651029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001923691,"about_ca_system_score_gemma":0.000247872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006929573,"about_ca_topic_score_gemma":0.0006968298,"domain_scores_codex":[0.997869,0.0008300176,0.0001670328,0.0002268774,0.000818604,0.00008835452],"domain_scores_gemma":[0.994402,0.003630947,0.0002887342,0.0002412621,0.001318732,0.0001182265],"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":[0.01274565,0.001643112,0.006020102,0.00192285,0.00070656,0.0005008028,0.0003945156,0.06159343,0.4564103,0.0003354483,0.0007217084,0.4570055],"study_design_scores_gemma":[0.0006098201,0.02249723,0.03694061,0.00009510547,0.001126469,0.001985469,0.0003466927,0.3619148,0.5708014,0.000299349,0.00322747,0.0001556258],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8842469,0.006363017,0.1053078,0.0001061983,0.0001235204,0.0004119047,0.0002549942,0.001133427,0.002052244],"genre_scores_gemma":[0.8472473,0.003571118,0.144684,0.00006969829,0.0001201841,0.0002035917,0.0008310537,0.0001716396,0.003101384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002879272,"threshold_uncertainty_score":0.01522726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03098980943056425,"score_gpt":0.3294634519440112,"score_spread":0.2984736425134469,"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."}}