{"id":"W2333871989","doi":"10.1109/taslp.2016.2546458","title":"Text-Dependent Speaker Recognition With Random Digit Strings","year":2016,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Audio Speech and Language Processing","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Normalization (sociology); Speech recognition; Computer science; String (physics); Pattern recognition (psychology); Logistic regression; Numerical digit; Speaker recognition; Random forest; Digit recognition; Artificial intelligence; Statistics; Mathematics; Machine learning; Arithmetic; Artificial neural network","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":[0.00156931,0.001192761,0.001075806,0.0008125989,0.0002782651,0.0006473042,0.001164168,0.000913414,0.004288937],"category_scores_gemma":[0.003236329,0.0002143479,0.001078024,0.0008190695,0.0002904717,0.001216672,0.001158106,0.00111258,0.006409357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002621933,"about_ca_system_score_gemma":0.0003973972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001393925,"about_ca_topic_score_gemma":0.001732748,"domain_scores_codex":[0.997907,0.0006338199,0.00009357567,0.0006744598,0.0005287515,0.0001623273],"domain_scores_gemma":[0.9984769,0.0005923804,0.00009838126,0.0003414189,0.0004200686,0.00007077809],"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":[0.001020933,0.000205984,0.003215779,0.0002849772,0.0003015625,0.0004415489,0.0001576147,0.02365145,0.122946,0.00228271,0.01294047,0.8325509],"study_design_scores_gemma":[0.00007483643,0.0004990316,0.008760137,0.00004140391,0.0001587044,0.001451796,0.0001289275,0.807633,0.1617919,0.005780447,0.01352964,0.0001501765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07859372,0.0015239,0.8982452,0.000414849,0.0006622517,0.0001543333,0.002926635,0.01333762,0.004141411],"genre_scores_gemma":[0.5285268,0.0005944993,0.4491371,0.0004906402,0.0004778665,0.0003037365,0.007781267,0.0005751689,0.01211293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004288937,"threshold_uncertainty_score":0.01434791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0164451531511052,"score_gpt":0.2346896929993656,"score_spread":0.2182445398482604,"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."}}