{"id":"W2530400070","doi":"10.1007/978-81-322-3592-7_9","title":"Robust Speaker Verification Using GFCC Based i-Vectors","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Garron Family Cancer Centre","keywords":"Speaker verification; NIST; Computer science; Speech recognition; Speaker recognition; Session (web analytics); Speaker diarisation; Channel (broadcasting); Pattern recognition (psychology); Cepstrum; Artificial intelligence; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0008615239,0.001408641,0.0009546221,0.0009899484,0.0005268885,0.001015006,0.0006923846,0.001745583,0.007832317],"category_scores_gemma":[0.0023603,0.000330301,0.0008992956,0.0006539605,0.0004219349,0.001272799,0.001057095,0.0009890859,0.01007997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001921377,"about_ca_system_score_gemma":0.0005194764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009984961,"about_ca_topic_score_gemma":0.001391719,"domain_scores_codex":[0.9989895,0.0002227008,0.00006082508,0.0002251098,0.000387071,0.0001147429],"domain_scores_gemma":[0.9990172,0.0003621835,0.00007640702,0.0002098749,0.0003056033,0.00002858923],"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":[0.0008033195,0.00005049642,0.0004438732,0.0002928961,0.00008544778,0.0002185359,0.00005808241,0.006331515,0.3948388,0.002440955,0.003534452,0.5909016],"study_design_scores_gemma":[0.0001076778,0.000664257,0.006319749,0.0002152986,0.0003092976,0.002689824,0.0001279386,0.3102998,0.6557514,0.004363711,0.01900048,0.0001506084],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0245235,0.002070905,0.9615484,0.0002253386,0.0004249357,0.0001021586,0.0006359214,0.004338214,0.006130531],"genre_scores_gemma":[0.3005508,0.00192484,0.6808451,0.0003611424,0.0003220181,0.0001972805,0.003008285,0.0006775141,0.01211292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007832317,"threshold_uncertainty_score":0.02620173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02807317074559154,"score_gpt":0.2094042920752328,"score_spread":0.1813311213296413,"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."}}