{"id":"W2006340605","doi":"10.1109/its.2014.6948012","title":"Investigating the use of modulation spectral features within an i-vector framework for far-field automatic speaker verification","year":2014,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Speech recognition; Computer science; Reverberation; Mel-frequency cepstrum; Speaker recognition; Focus (optics); Support vector machine; Pattern recognition (psychology); Modulation (music); Speaker verification; Artificial intelligence; Cepstrum; Field (mathematics); Feature extraction; Acoustics; Mathematics","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":[0.001055343,0.0004611823,0.0003542188,0.0004469453,0.0001939343,0.0006138898,0.0003731581,0.000453265,0.001388793],"category_scores_gemma":[0.002215568,0.0001176077,0.0002621458,0.0003110808,0.0003317695,0.0007831648,0.0004078425,0.0004058093,0.0005131481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001392736,"about_ca_system_score_gemma":0.0002759579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009189874,"about_ca_topic_score_gemma":0.0007780912,"domain_scores_codex":[0.9995205,0.0001850462,0.00002853701,0.00008129112,0.0001407787,0.00004374926],"domain_scores_gemma":[0.9991913,0.0004575156,0.00006699486,0.00007914707,0.0001825761,0.00002241395],"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":[0.0006490382,0.0002582745,0.002131845,0.0003150939,0.0001079553,0.0002011275,0.0002655311,0.07967301,0.2425965,0.01034163,0.000839704,0.6626204],"study_design_scores_gemma":[0.00002514158,0.001042237,0.00512799,0.00004127958,0.0000687428,0.0004164937,0.0001480866,0.8825574,0.103712,0.002742217,0.004062133,0.00005624172],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1607211,0.001267319,0.8352746,0.0001224732,0.00006481376,0.00008601697,0.00006098679,0.0006925141,0.001710223],"genre_scores_gemma":[0.778338,0.0006059028,0.2188742,0.00003691511,0.00005357121,0.00005729784,0.000203253,0.00006789807,0.001763122],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001388793,"threshold_uncertainty_score":0.00558126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04691116296599741,"score_gpt":0.2806821879547648,"score_spread":0.2337710249887674,"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."}}