{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006603311,0.00008593312,0.00009933343,0.00008507667,0.0001254553,0.0002044779,0.0004719592,0.00004399077,0.00002104137],"category_scores_gemma":[0.00004529315,0.00006048071,0.00005706359,0.0003012209,0.000009226446,0.0002862145,0.00004326292,0.00005794277,0.00001917244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002607461,"about_ca_system_score_gemma":0.00003579924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003150705,"about_ca_topic_score_gemma":0.0000841797,"domain_scores_codex":[0.9991125,0.00002915618,0.0001990031,0.0002338286,0.0001744061,0.0002510993],"domain_scores_gemma":[0.9993941,0.0002636367,0.00002615524,0.0002478388,0.00004620488,0.00002209533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003755041,0.0006479042,0.001513679,0.00006988796,0.00003273744,0.00001931138,0.001257431,0.05227524,0.001500023,0.4870155,0.006278275,0.4493525],"study_design_scores_gemma":[0.000207233,0.00001350846,0.0001605372,0.00001376077,0.000004408481,0.00001131782,0.00003533884,0.9706436,0.001129469,0.02492821,0.002753395,0.00009926672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01557712,0.00002883429,0.958699,0.0006841869,0.00009774796,0.0002082986,0.000001035337,0.00009919996,0.02460451],"genre_scores_gemma":[0.5721469,0.000001060772,0.4264828,0.0007777548,0.0000789223,0.00005234244,0.000002126501,0.00000590396,0.0004521649],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9183683,"threshold_uncertainty_score":0.2466332,"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."}}