{"id":"W2055696218","doi":"10.1109/odyssey.2006.248137","title":"The Geometry of the Channel Space in GMM-Based Speaker Recognition","year":2006,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Channel (broadcasting); Mixture model; Joint (building); Range (aeronautics); Rank (graph theory); Pattern recognition (psychology); Computer science; Gaussian; Factor (programming language); Artificial intelligence; Feature (linguistics); Factor analysis; Feature vector; Space (punctuation); Limiting; Mathematics; Machine learning; Combinatorics; Telecommunications; Physics; 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":[0.001543,0.0006565993,0.0005704378,0.0006669165,0.0005182175,0.00127254,0.0008549979,0.000831047,0.001890996],"category_scores_gemma":[0.006968176,0.0005817947,0.000573863,0.0008353629,0.002018941,0.002267112,0.001227589,0.001065135,0.001143089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008228104,"about_ca_system_score_gemma":0.0008436088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004944857,"about_ca_topic_score_gemma":0.003310614,"domain_scores_codex":[0.9981744,0.0009763929,0.00004072197,0.0003312519,0.0003547669,0.0001224718],"domain_scores_gemma":[0.9981272,0.001051291,0.000128854,0.0003255077,0.0002938816,0.00007332768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004654076,0.00004365844,0.003300422,0.0002572762,0.0001123749,0.0004094435,0.0007631994,0.2440335,0.03432257,0.3820055,0.007259893,0.3270268],"study_design_scores_gemma":[0.00001757101,0.000181628,0.003498919,0.00004565734,0.0000617029,0.0006923465,0.0001983559,0.7132601,0.02034917,0.2446889,0.01686585,0.0001398444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01282288,0.0006300974,0.983491,0.0002312042,0.00008594815,0.00002463292,0.0001798329,0.0005304106,0.002003918],"genre_scores_gemma":[0.5321747,0.001624725,0.4621616,0.0002147167,0.0003338754,0.0001285242,0.0005322601,0.0003817268,0.002447865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004944857,"threshold_uncertainty_score":0.009832144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01565638295550802,"score_gpt":0.2045470112893858,"score_spread":0.1888906283338778,"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."}}