{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005025937,0.0003265833,0.000431847,0.0009868353,0.000272719,0.0005522307,0.0002689604,0.0003168016,0.001334381],"category_scores_gemma":[0.001451645,0.0001199713,0.0002999888,0.000382505,0.000263872,0.000762925,0.0003263204,0.000345405,0.000897817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001613955,"about_ca_system_score_gemma":0.0001692491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003629151,"about_ca_topic_score_gemma":0.0003843463,"domain_scores_codex":[0.9993148,0.0001213631,0.0000285374,0.0002060237,0.0002852887,0.00004392225],"domain_scores_gemma":[0.9994494,0.0001919029,0.00005605023,0.00005196698,0.000231215,0.00001941175],"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":[0.0004129334,0.00004726178,0.006822641,0.0002542709,0.0001125253,0.000253948,0.0002669047,0.007692198,0.3976408,0.002009059,0.001162147,0.5833253],"study_design_scores_gemma":[0.00003361781,0.0008617978,0.08152739,0.00007001877,0.0002711241,0.003991388,0.0004380855,0.4627103,0.4324921,0.004367326,0.013026,0.0002108154],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2084481,0.001300637,0.7835273,0.0001217596,0.0001773042,0.0001001722,0.0001816265,0.001521583,0.004621616],"genre_scores_gemma":[0.8216579,0.0007230961,0.1732981,0.000039665,0.0001099921,0.00007118387,0.0003251652,0.0001296912,0.0036452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001334381,"threshold_uncertainty_score":0.004463911,"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."}}