{"id":"W1981185131","doi":"10.1109/iscslp.2014.6936584","title":"Speaker adaptive bottleneck features extraction for LVCSR based on discriminative learning of speaker codes","year":2014,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Speech recognition; Discriminative model; Speaker recognition; Hidden Markov model; Bottleneck; Speaker diarisation; Adaptation (eye); Word error rate; Pattern recognition (psychology); Feature extraction; Artificial intelligence; Task (project management); Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003964189,0.0001390691,0.0001936319,0.0001409084,0.0001053672,0.00005961017,0.0002220992,0.0000639219,0.0001455165],"category_scores_gemma":[0.0004035311,0.0001093459,0.0001237471,0.0001410415,0.00004695152,0.0002508733,0.00002811625,0.0001194402,0.00003826933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003037401,"about_ca_system_score_gemma":0.00002494805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002903319,"about_ca_topic_score_gemma":0.00002969708,"domain_scores_codex":[0.9989159,0.0001465512,0.0001764458,0.0003218623,0.0002553001,0.0001839822],"domain_scores_gemma":[0.9985616,0.0008372928,0.0001380778,0.0002235491,0.0001778846,0.00006160865],"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":[0.0002998801,0.000476725,0.0003312847,0.00005167291,0.00006335417,0.000003610667,0.001085894,0.001222613,0.0114539,0.1973315,0.00563314,0.7820464],"study_design_scores_gemma":[0.000960278,0.0009844549,0.02776575,0.0001160907,0.00003146357,0.000005776627,0.0007295284,0.7135882,0.2435087,0.004328373,0.007602441,0.0003788895],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005177617,0.000005997675,0.9304417,0.0006518315,0.0001713611,0.0002013483,0.000004373122,0.0001036444,0.06324213],"genre_scores_gemma":[0.8273231,0.000002364194,0.1701377,0.0003404254,0.00005792871,0.00002226852,0.000005155784,0.00001019,0.002100908],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8221455,"threshold_uncertainty_score":0.4458997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02734363352919382,"score_gpt":0.2756229960611707,"score_spread":0.2482793625319769,"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."}}