{"id":"W4229458071","doi":"10.18280/ts.390235","title":"Speaker Identification Based on Physical Variation of Speech Signal","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mel-frequency cepstrum; Speech recognition; Cepstrum; Speaker recognition; Computer science; Variation (astronomy); Identification (biology); Classifier (UML); Pattern recognition (psychology); Feature (linguistics); SIGNAL (programming language); Artificial intelligence; Feature extraction","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005896373,0.0001113577,0.000139912,0.0001846619,0.0001622934,0.00006086844,0.0004379322,0.00001863416,0.00228392],"category_scores_gemma":[0.00001370508,0.0001139283,0.0001144753,0.0003826716,0.00002204598,0.0001739623,0.00006362628,0.0001069955,0.00007242501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008545526,"about_ca_system_score_gemma":0.00005999527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001072652,"about_ca_topic_score_gemma":7.686022e-7,"domain_scores_codex":[0.9981947,0.0002091416,0.0002908517,0.0003172221,0.0008304938,0.0001576231],"domain_scores_gemma":[0.9992741,0.0001664747,0.0001777069,0.0002531877,0.00007581242,0.00005269976],"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":[0.0002219165,0.002907996,0.0004393738,0.00004814905,0.00009369457,0.000044018,0.00222121,0.01336383,0.2862416,0.04089088,0.002410689,0.6511166],"study_design_scores_gemma":[0.0008020298,0.0003968882,0.01971562,0.00001066211,0.00002337492,0.000005575078,0.00005866788,0.8881187,0.08661926,0.002589196,0.001448676,0.0002113927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2192411,0.000004350428,0.7746984,0.001479672,0.0002772963,0.0004200148,0.00004598185,0.0001458372,0.003687328],"genre_scores_gemma":[0.9925756,2.890189e-7,0.006739474,0.0003820921,0.0001198685,0.00006179355,0.0000249676,0.000008117753,0.00008772832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8747548,"threshold_uncertainty_score":0.9986281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02083586093241311,"score_gpt":0.2373790383508255,"score_spread":0.2165431774184124,"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."}}