{"id":"W4402910001","doi":"10.1016/j.csl.2024.101723","title":"Speech Generation for Indigenous Language Education","year":2024,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Multilingual Education and Policy","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University nuhelot'ine thaiyots'i nistameyimâkanak Blue Quills; National Research Council Canada","funders":"UK Research and Innovation","keywords":"Computer science; Indigenous; Natural language processing; Linguistics; Artificial intelligence; Speech recognition; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001887765,0.0005529678,0.000360048,0.0006703068,0.0007569917,0.00174554,0.001167854,0.0009714419,0.01925181],"category_scores_gemma":[0.005535805,0.0002986942,0.0006104067,0.0004346177,0.0007461011,0.001891157,0.003321418,0.001152316,0.00485056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001094543,"about_ca_system_score_gemma":0.001991637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006362997,"about_ca_topic_score_gemma":0.006753643,"domain_scores_codex":[0.9986914,0.0004287445,0.00009564393,0.0002178617,0.0004737498,0.00009276142],"domain_scores_gemma":[0.9982047,0.0008624113,0.00006239436,0.0003234152,0.0004327707,0.0001142771],"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":[0.0002768289,0.0001422594,0.002334797,0.001093188,0.00006352599,0.0005534301,0.004831803,0.01853328,0.05926726,0.0462144,0.03237985,0.8343093],"study_design_scores_gemma":[0.0001433603,0.0004791406,0.006114885,0.0006365014,0.0001370993,0.001401694,0.002895623,0.1762574,0.09048539,0.07777399,0.643478,0.0001968481],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0350126,0.001112659,0.8660226,0.002190396,0.0004111069,0.0004704629,0.00195783,0.03197363,0.06084866],"genre_scores_gemma":[0.383041,0.001349992,0.5565807,0.0006845545,0.0001627689,0.0006846293,0.004962873,0.004882764,0.0476508],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01925181,"threshold_uncertainty_score":0.06440365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04128638080843477,"score_gpt":0.4452927727766668,"score_spread":0.404006391968232,"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."}}