{"id":"W7110691195","doi":"","title":"Comparing language-specific and cross-language acoustic models for low-resource phonetic forced alignment","year":2025,"lang":"en","type":"article","venue":"ScholarSpace (University of Hawaii at Manoa)","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Phone; Acoustic model; Homogeneous; Hidden Markov model; Stress (linguistics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003046338,0.0001628068,0.0002842596,0.0002494443,0.0004485408,0.000148917,0.0006395107,0.00009554916,0.00005597237],"category_scores_gemma":[0.00002298057,0.0002014241,0.0001231931,0.000277828,0.0001321884,0.0005424288,0.0005523279,0.0001275852,0.00002202852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001274,"about_ca_system_score_gemma":0.00003132442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004179542,"about_ca_topic_score_gemma":0.0001313106,"domain_scores_codex":[0.9988056,0.00006256605,0.0001150403,0.000476386,0.0002420751,0.0002982779],"domain_scores_gemma":[0.9990383,0.0001711212,0.0001068394,0.0004848141,0.00008415148,0.0001147631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0017355,0.001154534,0.007263174,0.002589256,0.001187757,0.0006874622,0.06936905,0.02095391,0.4985853,0.1087718,0.03927108,0.2484312],"study_design_scores_gemma":[0.01442627,0.0003560673,0.02459424,0.001182578,0.0003308774,0.00007601367,0.07715526,0.7721041,0.08318463,0.00325177,0.02126154,0.002076606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6426304,0.0005654063,0.3484086,0.000726638,0.00009202908,0.0003137903,0.00001405517,0.0001103728,0.007138746],"genre_scores_gemma":[0.9633616,0.00005574467,0.02711978,0.0001251311,0.00001171787,0.000001658603,0.000008096826,0.000009679602,0.009306573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7511503,"threshold_uncertainty_score":0.8213837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0166982691697692,"score_gpt":0.2336168389856255,"score_spread":0.2169185698158563,"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."}}