{"id":"W2992710831","doi":"","title":"The RACAD speech corpus of New Brunswick Acadian French: Design and applications","year":2008,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Linguistic Variation and Morphology","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton; University of New Brunswick","funders":"Defense Advanced Research Projects Agency; University of Cambridge; New Brunswick Innovation Foundation; Université de Moncton","keywords":"Phone; Computer science; Speech corpus; Speech recognition; Variation (astronomy); Corpus linguistics; Natural language processing; Word (group theory); Text corpus; Linguistics; Artificial intelligence; Speech synthesis","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001185806,0.0007276345,0.0003882659,0.00154072,0.0009632477,0.001022866,0.001125441,0.0005686014,0.01076014],"category_scores_gemma":[0.002620558,0.0002714839,0.0002809402,0.001631547,0.000647347,0.0005065804,0.001162765,0.000310358,0.003018773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00176414,"about_ca_system_score_gemma":0.002625874,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1469172,"about_ca_topic_score_gemma":0.1658355,"domain_scores_codex":[0.9991713,0.0002676689,0.00007989626,0.0002448283,0.0001622058,0.00007407421],"domain_scores_gemma":[0.9983928,0.0004045656,0.00005386607,0.0001705178,0.00086616,0.0001120403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003702208,0.0008837372,0.03110328,0.003479942,0.0002182348,0.005403602,0.009137294,0.01479506,0.1235693,0.005581791,0.06255049,0.7395752],"study_design_scores_gemma":[0.002023667,0.002584067,0.268182,0.0005097367,0.0004370637,0.004614528,0.02011968,0.07937177,0.1080061,0.003130063,0.5105107,0.0005105635],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7883428,0.002545238,0.087249,0.0008990925,0.0002954764,0.01358246,0.06795288,0.00686885,0.03226414],"genre_scores_gemma":[0.6178964,0.001598858,0.1814761,0.0004579507,0.0001143302,0.03036409,0.1360618,0.0008682358,0.03116232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8530828,"threshold_uncertainty_score":0.292124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04087017483337119,"score_gpt":0.2790398882007486,"score_spread":0.2381697133673774,"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."}}