{"id":"W2994526441","doi":"","title":"A formant frequency estimator for noisy speech based on correlation and cepstrum","year":2008,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Formant; Cepstrum; Vocal tract; Frequency domain; Estimator; Speech recognition; Computer science; Noise (video); Mathematics; Artificial intelligence; Statistics","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":[0.0008710034,0.0005805566,0.0007038846,0.0007903463,0.0002582705,0.0004471616,0.0005534518,0.0007497776,0.000993938],"category_scores_gemma":[0.002252755,0.0003536921,0.0004364929,0.0005531341,0.0002830852,0.0008101318,0.0003112102,0.000650291,0.0008987482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002374056,"about_ca_system_score_gemma":0.0005250418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001480077,"about_ca_topic_score_gemma":0.001928806,"domain_scores_codex":[0.999669,0.00007241206,0.00002180216,0.00007772231,0.0001384804,0.00002059143],"domain_scores_gemma":[0.9993839,0.0002271921,0.0000813391,0.00007726956,0.0002121822,0.00001804868],"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":[0.0003175648,0.00009798924,0.001629272,0.0003186139,0.0001006948,0.0002066891,0.0001393133,0.07121569,0.3041934,0.005890734,0.001194823,0.6146953],"study_design_scores_gemma":[0.00002258455,0.0001861927,0.002987295,0.00002653229,0.00005906678,0.0005670906,0.00001941936,0.9132161,0.07707265,0.001060781,0.004723815,0.00005837936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007094364,0.000176233,0.9921819,0.00001428975,0.00002026939,0.00002064608,0.00001748951,0.0003074376,0.0001672871],"genre_scores_gemma":[0.09392021,0.0002527848,0.9047122,0.00001397865,0.00003138668,0.00005746489,0.00008391528,0.00006411931,0.0008640008],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001480077,"threshold_uncertainty_score":0.004606366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01476931278590884,"score_gpt":0.2144460450366402,"score_spread":0.1996767322507313,"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."}}