{"id":"W7074640239","doi":"","title":"Speech recognition &amp; diphone extraction for natural speech synthesis","year":2009,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Voice activity detection; Speech synthesis; Selection (genetic algorithm); Speech processing; Audio mining; Natural (archaeology); Linear predictive coding; Natural language","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":[0.0008946804,0.001093288,0.0006362106,0.001490149,0.0005758692,0.001821756,0.0009359564,0.0006655669,0.04258893],"category_scores_gemma":[0.002796743,0.0006467337,0.000808002,0.0009192803,0.000456045,0.001232261,0.0008284438,0.001215385,0.02864812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008035516,"about_ca_system_score_gemma":0.001231273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002734998,"about_ca_topic_score_gemma":0.005029077,"domain_scores_codex":[0.9989929,0.0001809643,0.00008979472,0.0003214446,0.0003448999,0.00007008002],"domain_scores_gemma":[0.99915,0.0002711008,0.00008129742,0.0001782348,0.0002858808,0.00003343275],"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":[0.0001702093,0.00006646183,0.0007958048,0.0005311561,0.00005583607,0.0001553819,0.0002497497,0.00554234,0.1234507,0.02250741,0.0410248,0.8054501],"study_design_scores_gemma":[0.0001030474,0.000224441,0.007160726,0.000275749,0.0001418709,0.0008052948,0.0003078225,0.1901662,0.3551601,0.02373374,0.4217493,0.0001716507],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006602059,0.000718772,0.9561006,0.0003104828,0.0002699346,0.0002160305,0.002617353,0.01294972,0.02021503],"genre_scores_gemma":[0.05269942,0.0008822841,0.9118662,0.000157251,0.000141974,0.0003114609,0.005017443,0.001428419,0.02749547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04258893,"threshold_uncertainty_score":0.1424741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02986714634982909,"score_gpt":0.1887179885735893,"score_spread":0.1588508422237602,"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."}}