{"id":"W7130678234","doi":"10.1109/swc65939.2025.00066","title":"Augmenting Japanese Language Acquisition via LLMs and ASR","year":2025,"lang":"","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; Athabasca University","keywords":"Active listening; Japanese language; Language acquisition; Kanji","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004783672,0.0002037406,0.0002264268,0.0002546458,0.0002867443,0.0005726927,0.0003273936,0.0001227719,0.002113674],"category_scores_gemma":[0.00006598836,0.0001957903,0.00008881235,0.0005641902,0.00007878795,0.0004866378,0.0003583862,0.0001258987,0.0003088891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004128783,"about_ca_system_score_gemma":0.00004595723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001903367,"about_ca_topic_score_gemma":0.00003165862,"domain_scores_codex":[0.9983904,0.0001316048,0.0003386616,0.0005565141,0.0002341083,0.0003486959],"domain_scores_gemma":[0.9991115,0.0002011362,0.00008439134,0.000395508,0.00008925555,0.0001182219],"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":[0.000009100319,0.000096248,0.0002649876,0.00007450359,0.00005202307,0.0000345744,0.002447026,4.648373e-7,0.009392512,0.002561089,0.0008053954,0.984262],"study_design_scores_gemma":[0.003150947,0.0002550009,0.02619959,0.001184199,0.0002973245,0.0002300313,0.01219102,0.7186928,0.2253009,0.006407023,0.004332989,0.001758164],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3480758,0.001769792,0.3946008,0.00468781,0.001491036,0.0004497319,0.000006056631,0.000334121,0.2485848],"genre_scores_gemma":[0.9616436,0.00008521104,0.01630677,0.002783121,0.00008089414,0.00001151447,0.000003091401,0.000007452359,0.0190783],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9825039,"threshold_uncertainty_score":0.9987985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009253066351147973,"score_gpt":0.25425679137887,"score_spread":0.245003725027722,"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."}}