{"id":"W2141473650","doi":"10.1109/newcas.2007.4487955","title":"A bifeature voiced/unvoiced discrimination algorithm for speech signals in the presense of noise","year":2007,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Speech recognition; Computer science; Noise (video); Speech enhancement; Energy (signal processing); Residual; SIGNAL (programming language); Signal-to-noise ratio (imaging); Speech processing; Algorithm; Noise measurement; Pattern recognition (psychology); Artificial intelligence; Noise reduction; Mathematics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.001130333,0.00009664131,0.0001205309,0.0001432513,0.00006163445,0.00008986743,0.0005925705,0.00006249129,0.000004377382],"category_scores_gemma":[0.00007677307,0.00006096018,0.00005738759,0.000554446,0.00002820811,0.0003861773,0.00005907928,0.00009621651,0.000003358968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001636872,"about_ca_system_score_gemma":0.00003439326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002186069,"about_ca_topic_score_gemma":0.00006789062,"domain_scores_codex":[0.9989555,0.00003321016,0.0002304858,0.0002272412,0.0002946497,0.0002588468],"domain_scores_gemma":[0.9990795,0.0003753677,0.00009747619,0.0002806713,0.0001312964,0.00003568377],"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":[0.00001133078,0.0001147903,0.00009038641,0.00003717999,0.000004900968,0.00001464646,0.001610454,0.00001575241,0.038501,0.000807235,0.001753249,0.9570391],"study_design_scores_gemma":[0.0007422358,0.0001346762,0.005404486,0.0000636775,0.000006963766,0.00002705618,0.0004638355,0.01936295,0.9654908,0.006026603,0.002098216,0.0001784985],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02531461,0.0003189649,0.9700194,0.001317145,0.00009059576,0.0003571607,0.000002495274,0.00004029306,0.002539347],"genre_scores_gemma":[0.4083491,0.00001024552,0.5904805,0.0005859006,0.0001111657,0.00001492435,0.000003301698,0.000007030092,0.0004378122],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9568606,"threshold_uncertainty_score":0.2485884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02027682138007711,"score_gpt":0.2942287018344458,"score_spread":0.2739518804543687,"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."}}