{"id":"W2074447895","doi":"10.1109/ijcnn.2009.5178984","title":"Voice-based gender identification via multiresolution frame classification of spectro-temporal maps","year":2009,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Spectrogram; Classifier (UML); Pattern recognition (psychology); Artificial intelligence; Feature extraction; Weighting; Subspace topology; Speech recognition; Identification (biology)","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":[],"consensus_categories":[],"category_scores_codex":[0.0002844874,0.0001047399,0.0001125823,0.0001469464,0.00009363408,0.0001096037,0.0004198641,0.00007421317,0.00001390794],"category_scores_gemma":[0.00003102731,0.00009833733,0.00005855114,0.00043052,0.00003108168,0.0005661416,0.00001860202,0.00008711999,0.00007143263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006375254,"about_ca_system_score_gemma":0.00007757019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001674358,"about_ca_topic_score_gemma":0.000005707765,"domain_scores_codex":[0.9987844,0.00004147039,0.0003343105,0.0003314914,0.0003114675,0.0001968152],"domain_scores_gemma":[0.9990757,0.00002327656,0.0002418076,0.000449701,0.0001506121,0.00005896604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001266722,0.0001985985,0.002276226,0.00002182069,0.00000559609,0.000001372203,0.0001665786,0.0001241906,0.8888468,0.007411334,0.0007154998,0.1002194],"study_design_scores_gemma":[0.000334789,0.00006079667,0.1514694,0.00001308527,0.000005957696,0.000003146853,0.0000183258,0.1534135,0.6794013,0.01480756,0.0003143511,0.0001578406],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04103834,0.00007835545,0.9553173,0.001594386,0.0001183588,0.0001252745,9.451903e-7,0.000192598,0.001534423],"genre_scores_gemma":[0.8680685,0.000001787502,0.1314866,0.0002602369,0.00004704441,0.000003565962,0.00001920121,0.00000382841,0.0001093295],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8270301,"threshold_uncertainty_score":0.401008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03051265856726352,"score_gpt":0.2710844103676076,"score_spread":0.240571751800344,"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."}}