{"id":"W2917672101","doi":"","title":"Voice biometrics distinction between English and Arabic using Sound Cleaner Filtering and SpeechPro SIS II analysis","year":2018,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Formant; Vowel; Speech recognition; Computer science; Speaker recognition; Spectrogram; Software; Articulation (sociology); Sound quality; Arabic; Biometrics; Linguistics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008490437,0.0004303259,0.0003121738,0.002596373,0.0004207154,0.001244232,0.0001821351,0.0005209153,0.003470438],"category_scores_gemma":[0.003212577,0.0001503651,0.0003720637,0.0008392084,0.0004188707,0.0008252749,0.0006089877,0.0003519115,0.001424231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002292097,"about_ca_system_score_gemma":0.0002614382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001004074,"about_ca_topic_score_gemma":0.001436608,"domain_scores_codex":[0.9992198,0.0001361682,0.0001013846,0.0001788057,0.0002971049,0.00006679702],"domain_scores_gemma":[0.9986024,0.0004769761,0.0001977819,0.0001079621,0.0005351756,0.00007964619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001407491,0.0001763124,0.09843459,0.0003027863,0.0001020282,0.0007590828,0.002516265,0.0008460326,0.3823834,0.002065039,0.001451997,0.5095549],"study_design_scores_gemma":[0.00005866359,0.001246474,0.7560862,0.0001574429,0.0002751275,0.005552494,0.006547324,0.02887722,0.1855495,0.002334398,0.01314517,0.0001699951],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9084611,0.0006517556,0.07812407,0.0001942133,0.0001517395,0.0001756741,0.0005184737,0.000471496,0.01125154],"genre_scores_gemma":[0.9395781,0.0003154661,0.0561475,0.00007214877,0.0000407068,0.0001026025,0.0004162457,0.00005927641,0.003268047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003470438,"threshold_uncertainty_score":0.01160979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05138435495405243,"score_gpt":0.2786003491946247,"score_spread":0.2272159942405723,"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."}}