{"id":"W4416610221","doi":"10.48550/arxiv.2505.23170","title":"ZIPA: A family of efficient models for multilingual phone recognition","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Phone; Leverage (statistics); Training set; Set (abstract data type); Noisy data; Data set","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001791763,0.002078814,0.001349952,0.001265512,0.0007368691,0.001710651,0.003162515,0.0014421,0.008122645],"category_scores_gemma":[0.007397872,0.001004277,0.001638936,0.0011292,0.0005906122,0.002699395,0.003163799,0.003455599,0.01149642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008021531,"about_ca_system_score_gemma":0.001574331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006196272,"about_ca_topic_score_gemma":0.009744847,"domain_scores_codex":[0.998807,0.0003290169,0.00007115951,0.0003910796,0.0002975109,0.0001041487],"domain_scores_gemma":[0.9979997,0.00090256,0.00008756888,0.0004707008,0.0004289969,0.0001104396],"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":[0.0006152875,0.0002048092,0.002580171,0.0003410078,0.0002920664,0.0002158998,0.0002019715,0.3348686,0.01384802,0.0126583,0.03162571,0.6025482],"study_design_scores_gemma":[0.00001706719,0.000053239,0.000197413,0.0000144391,0.00002268078,0.00007513561,0.00002010832,0.9828641,0.002911448,0.00751124,0.006293281,0.0000197265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006786182,0.0007497683,0.9760307,0.0002710224,0.0002024638,0.0000939028,0.001798937,0.01214722,0.001919863],"genre_scores_gemma":[0.2744142,0.001640689,0.6845278,0.0008480059,0.0004614122,0.001074168,0.01676524,0.003778021,0.01649054],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008122645,"threshold_uncertainty_score":0.02717298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1281203376629262,"score_gpt":0.3082748997877475,"score_spread":0.1801545621248213,"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."}}