{"id":"W2129269533","doi":"","title":"VARIABLE PRE-EMPHASIS LPC FOR MODELING VOCAL EFFORT IN THE SINGING VOICE","year":2006,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Formant; Spectral envelope; Speech recognition; Computer science; Envelope (radar); Singing; Phonation; Filter (signal processing); Linear predictive coding; Voice analysis; Breathy voice; Acoustics; Speech processing; Vowel; Telecommunications; Audiology; Physics","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":[0.0006246295,0.000795493,0.0002840694,0.0005416852,0.0003645902,0.0004801359,0.0007902559,0.0009308974,0.002358222],"category_scores_gemma":[0.002029641,0.0002305353,0.0003501834,0.0009573596,0.0003312657,0.0004110926,0.000238281,0.0009522394,0.001202475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004482299,"about_ca_system_score_gemma":0.0005371912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007298703,"about_ca_topic_score_gemma":0.007497764,"domain_scores_codex":[0.9997008,0.00009852497,0.00001192011,0.00005630619,0.0001135754,0.00001891955],"domain_scores_gemma":[0.9994019,0.0003197119,0.00004034353,0.00006411065,0.0001619998,0.00001188442],"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":[0.0003557501,0.00009619119,0.001849019,0.000352828,0.00006610814,0.0003536323,0.0001888128,0.3891803,0.07156781,0.01244956,0.00514976,0.5183903],"study_design_scores_gemma":[0.00000587054,0.00004341371,0.000785413,0.00001668679,0.00001776278,0.00006499077,0.000007537649,0.9879885,0.007229008,0.0009812847,0.002844429,0.00001510302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01115525,0.0005602958,0.9853349,0.00009142095,0.00007500278,0.00005511026,0.0001283224,0.0009504376,0.00164937],"genre_scores_gemma":[0.3137877,0.001098191,0.6787866,0.00008748885,0.00009341818,0.0002179734,0.0005549188,0.0004330362,0.004940712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007298703,"threshold_uncertainty_score":0.01451248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0234487556119741,"score_gpt":0.2564090423091711,"score_spread":0.232960286697197,"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."}}