{"id":"W2125199437","doi":"10.1109/mwscas.2007.4488548","title":"A formant frequency estimation algorithm for speech signals with low signal-to-noise ratio","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","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; Speech recognition; Computer science; Autocorrelation; Vocal tract; Impulse response; Autoregressive model; Noise (video); Speech processing; Signal-to-noise ratio (imaging); Algorithm; Mathematics; Artificial intelligence; Statistics; 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":[0.0009241747,0.001001625,0.0008383329,0.001047848,0.0004293757,0.0005948693,0.0009519927,0.001211261,0.002899479],"category_scores_gemma":[0.002493705,0.0004640792,0.0006453109,0.0006442617,0.0003781592,0.001246265,0.0004497723,0.001186091,0.002891779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003038736,"about_ca_system_score_gemma":0.0006316846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001018824,"about_ca_topic_score_gemma":0.001265794,"domain_scores_codex":[0.9994784,0.0000843572,0.0000424517,0.0001531869,0.0002148378,0.00002685517],"domain_scores_gemma":[0.9992062,0.0003140559,0.00008061984,0.0001047159,0.0002747559,0.00001962127],"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":[0.0001975948,0.00008268363,0.0003821175,0.0002376213,0.0000738648,0.00009260463,0.0001126978,0.02399651,0.1335706,0.004861041,0.001964142,0.8344285],"study_design_scores_gemma":[0.00006599565,0.0002198684,0.00206517,0.00006022293,0.00009036421,0.0006916146,0.00004470363,0.8655298,0.1035384,0.004358273,0.02325756,0.00007789183],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001313742,0.0001132645,0.9978898,0.00001596156,0.00002738928,0.00002229582,0.0000154682,0.0004557212,0.0001462738],"genre_scores_gemma":[0.01515856,0.00015941,0.9833056,0.00002220752,0.00003958168,0.00007968338,0.00009689826,0.0001187647,0.001019232],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002899479,"threshold_uncertainty_score":0.009699702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01668165069741968,"score_gpt":0.25972919146413,"score_spread":0.2430475407667104,"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."}}