{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006872565,0.0008145905,0.0004308845,0.0004083184,0.0002233998,0.0007727131,0.0007632563,0.0004329343,0.008436276],"category_scores_gemma":[0.001761033,0.0002616594,0.0002627554,0.0002385797,0.0003860483,0.00126642,0.001564631,0.0005795175,0.004400955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002045416,"about_ca_system_score_gemma":0.0004425955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001931739,"about_ca_topic_score_gemma":0.004751816,"domain_scores_codex":[0.999501,0.0001318438,0.00002668737,0.0001273772,0.0001495443,0.00006358894],"domain_scores_gemma":[0.9993741,0.0002367234,0.00003162567,0.0001045921,0.0001986234,0.00005450327],"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":[0.0003431032,0.0002534926,0.00209681,0.0002908589,0.00002378698,0.0004460974,0.001439054,0.002927815,0.2836005,0.001806884,0.003059009,0.7037125],"study_design_scores_gemma":[0.0003416888,0.004198528,0.02006711,0.0001881605,0.0002816559,0.003156402,0.002792292,0.2064016,0.5894187,0.005650998,0.1672217,0.0002810859],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3720477,0.0005694393,0.5520769,0.0003900141,0.0001800542,0.0004291977,0.0003355863,0.02242426,0.05154676],"genre_scores_gemma":[0.6300606,0.0003074794,0.3475311,0.0002765293,0.00005059859,0.0002648387,0.0004378474,0.001036971,0.02003396],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008436276,"threshold_uncertainty_score":0.02822208,"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."}}