{"id":"W2141401351","doi":"10.1109/mmsp.2007.4412892","title":"Combining Vocal Source and MFCC Features for Enhanced Speaker Recognition Performance Using GMMs","year":2007,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mel-frequency cepstrum; Speech recognition; Crest factor; Computer science; Cepstrum; Vocal tract; Mixture model; Pattern recognition (psychology); Artificial intelligence; Speaker recognition; Centroid; Feature extraction; Bandwidth (computing)","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":[0.001094305,0.00102143,0.000883335,0.001513427,0.0003009889,0.0006288871,0.000488428,0.0005456413,0.001881427],"category_scores_gemma":[0.001937399,0.0003343382,0.0005460121,0.000607437,0.0002203934,0.001043536,0.0005730235,0.0005111551,0.001649606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002534157,"about_ca_system_score_gemma":0.000310854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001932395,"about_ca_topic_score_gemma":0.003740488,"domain_scores_codex":[0.9993052,0.000168523,0.00003787418,0.0001390198,0.0002727017,0.00007662303],"domain_scores_gemma":[0.9993824,0.0002326289,0.00004299486,0.00005974758,0.0002568174,0.0000253189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004013096,0.0001264377,0.002501822,0.0001784581,0.0001351343,0.000105502,0.0001143083,0.01427927,0.1980744,0.00087459,0.002336312,0.7808724],"study_design_scores_gemma":[0.00006503174,0.0005956291,0.03211421,0.00007059263,0.0004925338,0.00109383,0.0001539908,0.6765212,0.2689537,0.002136556,0.01754914,0.0002534919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.14234,0.00217456,0.8439554,0.0001996993,0.0002513513,0.00008934105,0.0002872403,0.007593943,0.003108462],"genre_scores_gemma":[0.6278091,0.0009197657,0.3667532,0.00009401978,0.0002911699,0.0001040527,0.0006536057,0.00039541,0.002979661],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001932395,"threshold_uncertainty_score":0.006293952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03914357583190128,"score_gpt":0.2683018335022468,"score_spread":0.2291582576703455,"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."}}