{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00125313,0.001187235,0.001243344,0.001890328,0.0008015644,0.001156461,0.001762543,0.001069075,0.01907858],"category_scores_gemma":[0.003044283,0.0005694816,0.001645703,0.002657415,0.0007732265,0.001831479,0.001038408,0.002122844,0.02175196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007668863,"about_ca_system_score_gemma":0.0008867072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002648313,"about_ca_topic_score_gemma":0.002828498,"domain_scores_codex":[0.9980259,0.0002178031,0.0001362863,0.0005005843,0.001013027,0.0001062871],"domain_scores_gemma":[0.9991359,0.0001918285,0.00007081407,0.0002035469,0.0003817895,0.00001619474],"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":[0.0001225263,0.00006339206,0.0003976367,0.0003613654,0.00008898044,0.0002199227,0.0001711091,0.0124506,0.06455082,0.02558838,0.02022728,0.8757581],"study_design_scores_gemma":[0.00005841912,0.0002663248,0.0061619,0.0002742438,0.0001553339,0.003088746,0.0002812192,0.3566322,0.1347388,0.05058572,0.4474829,0.0002742253],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002180218,0.002655242,0.9801165,0.0002714749,0.0003767672,0.0001244758,0.000373673,0.003465754,0.01043589],"genre_scores_gemma":[0.06674793,0.006159504,0.8832614,0.0005118273,0.0006925392,0.0006545749,0.003171256,0.001648151,0.03715296],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01907858,"threshold_uncertainty_score":0.06382424,"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."}}