{"id":"W7132304290","doi":"","title":"iFLYTEK: Can the Leader in Intelligent Speech Recognition Succeed in the Era of Large Language Models?","year":2024,"lang":"","type":"other","venue":"CEIBS Institutional Repository","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Casa","funders":"","keywords":"Feature (linguistics); Natural language; Speaker recognition; Speech technology; Field (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003333294,0.001130707,0.001032224,0.001287369,0.0004219322,0.0002755451,0.001747871,0.001069558,0.0007898787],"category_scores_gemma":[0.0003794273,0.0007908518,0.0006352171,0.002249511,0.002087211,0.0004352557,0.0003893721,0.004452929,0.002909247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00246105,"about_ca_system_score_gemma":0.002740857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004481487,"about_ca_topic_score_gemma":0.01081003,"domain_scores_codex":[0.9913856,0.001421551,0.002219085,0.001527318,0.002329481,0.001116927],"domain_scores_gemma":[0.9966858,0.0003919345,0.0008905368,0.001544293,0.0003327237,0.0001547242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004919728,0.02291017,0.003799112,0.01210828,0.005925797,0.04038947,0.3200395,0.03804651,0.04282835,0.3221373,0.1348584,0.05203736],"study_design_scores_gemma":[0.01478671,0.001358693,0.01461441,0.0884335,0.005012178,0.01523821,0.1739091,0.05920224,0.08018465,0.06801512,0.4662198,0.01302534],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1658032,0.03572944,0.0002030991,0.001015952,0.005708417,0.004416341,0.002089217,0.0001584751,0.7848759],"genre_scores_gemma":[0.9685346,0.0004483152,0.0002132865,0.0004775893,0.001674185,0.000441621,0.0006139643,0.0005276321,0.02706875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8027315,"threshold_uncertainty_score":0.9994543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03240390283629502,"score_gpt":0.2821756730713068,"score_spread":0.2497717702350117,"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."}}