{"id":"W7001204461","doi":"","title":"Issues of bilingualism in likelihood ratio-based forensic voice comparison","year":2021,"lang":"en","type":"dissertation","venue":"White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Formant; Linear discriminant analysis; Matching (statistics); Situated; Neuroscience of multilingualism; Speaker recognition; Software","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07026698,0.0006691578,0.0009426766,0.00136409,0.001188184,0.004491027,0.001577267,0.001168487,0.004190502],"category_scores_gemma":[0.183109,0.0005651445,0.0005504721,0.0008914036,0.005388106,0.004582644,0.004704303,0.001840052,0.001165266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001211361,"about_ca_system_score_gemma":0.002046641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00326429,"about_ca_topic_score_gemma":0.003981797,"domain_scores_codex":[0.9603795,0.02982653,0.001659969,0.002727378,0.004811338,0.0005953303],"domain_scores_gemma":[0.89075,0.08631583,0.006721979,0.00834697,0.00686385,0.001001332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003017973,0.0002840486,0.1228161,0.0008609068,0.0004161204,0.001358556,0.01773216,0.009520455,0.03427429,0.1570669,0.002412487,0.6502401],"study_design_scores_gemma":[0.0007496089,0.004741763,0.2250085,0.001462122,0.0005668458,0.01199016,0.01549239,0.143329,0.09305853,0.4560216,0.04648883,0.001090833],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4265699,0.006017434,0.5116956,0.01134913,0.0005367371,0.0003148059,0.0002831455,0.001094278,0.04213888],"genre_scores_gemma":[0.9122904,0.0008635982,0.08339499,0.0007086791,0.0002357862,0.0001531502,0.00008824882,0.0003161331,0.001949079],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07026698,"threshold_uncertainty_score":0.3716117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0240817240188065,"score_gpt":0.2436545374931164,"score_spread":0.2195728134743099,"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."}}