{"id":"W4386626719","doi":"10.18280/isi.280429","title":"A Fractional Ebola Optimization Search Algorithm Approach for Enhanced Speaker Diarization","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Speaker diarisation; Computer science; Algorithm; Speech recognition; Speaker verification; Speaker recognition","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006491649,0.0001458811,0.0001517872,0.0005000084,0.0003852618,0.0005225264,0.0002939911,0.000122953,0.0000584667],"category_scores_gemma":[0.0002821074,0.0001484203,0.00008472136,0.001137481,0.00004642171,0.004323006,0.00007529668,0.00008819049,0.0002622067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001639449,"about_ca_system_score_gemma":0.0001037479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001083556,"about_ca_topic_score_gemma":4.516983e-7,"domain_scores_codex":[0.9985956,0.00006149265,0.000430466,0.0002023276,0.0004029449,0.0003071994],"domain_scores_gemma":[0.9988669,0.000140412,0.0001695931,0.0002308817,0.000510551,0.00008173491],"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":[0.0000217538,0.0000461594,0.0000249664,0.0001555935,0.00004208038,6.619717e-7,0.003577599,0.06925888,0.0003347835,0.004925972,0.00129372,0.9203178],"study_design_scores_gemma":[0.0003778526,0.000036719,0.0004925157,0.00002315486,0.000006063065,0.00001124994,0.0004007626,0.9901555,0.005618311,0.001534451,0.001157057,0.0001864276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000943759,0.000005890929,0.9876256,0.0000807863,0.0003196292,0.0005723964,0.00003284498,0.0005979162,0.009821206],"genre_scores_gemma":[0.09142999,0.00003698282,0.906045,0.0002450107,0.0001463709,0.0004142618,0.0013316,0.00001696121,0.0003337953],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9208966,"threshold_uncertainty_score":0.6052404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02571367745589117,"score_gpt":0.2521937344669724,"score_spread":0.2264800570110812,"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."}}