{"id":"W1589469711","doi":"10.5772/6388","title":"Normalization and Transformation Techniques for Robust Speaker Recognition","year":2008,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Speaker recognition; Normalization (sociology); Speech recognition; Computer science; Speaker diarisation; Identity (music); Task (project management); Artificial intelligence; Pattern recognition (psychology); Frame (networking); Feature (linguistics); Transformation (genetics); Linguistics; Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.0001904315,0.0002702778,0.0002575692,0.0003545763,0.0001492168,0.0001218557,0.000240661,0.0003769477,0.00005345308],"category_scores_gemma":[0.00002631253,0.0002733122,0.0001372251,0.00001923938,0.00007810408,0.0002904213,0.00004260146,0.000183117,0.00004907042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006106867,"about_ca_system_score_gemma":0.0000509504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004986722,"about_ca_topic_score_gemma":0.00001721708,"domain_scores_codex":[0.9988253,0.00001485604,0.0004001766,0.000369678,0.0002173572,0.0001726474],"domain_scores_gemma":[0.9991191,0.0000681669,0.0001956678,0.0002522849,0.0002951466,0.00006963786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001262891,0.000006169329,1.35086e-7,0.00005871662,0.00002254671,0.000004999053,0.0003134546,2.849424e-8,0.0005326976,0.006212993,0.0006914391,0.9921442],"study_design_scores_gemma":[0.0004450094,0.0002642273,0.000002844715,0.000772321,0.00007062665,0.0004190964,0.00001903274,0.00219486,0.288718,0.0724334,0.6337292,0.0009313384],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000006994447,0.00003882494,0.600107,0.00009637915,0.0001027229,0.0005741452,0.00003434506,0.0003399211,0.3986996],"genre_scores_gemma":[0.006897351,0.001897291,0.7448815,0.002142523,0.000711577,0.0007301043,0.0005600885,0.0002281138,0.2419515],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9912128,"threshold_uncertainty_score":0.9999719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05414298100150217,"score_gpt":0.2391745322329087,"score_spread":0.1850315512314066,"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."}}