{"id":"W2163786726","doi":"10.1007/s11265-015-1012-6","title":"Speaker Adaptation of Hybrid NN/HMM Model for Speech Recognition Based on Singular Value Decomposition","year":2015,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Speech recognition; Singular value decomposition; TIMIT; Hidden Markov model; Adaptation (eye); Vocabulary; Speaker recognition; Pattern recognition (psychology); Artificial intelligence; Artificial neural network; Task (project management)","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":[0.001959316,0.000160931,0.0003709099,0.0004018417,0.00009769736,0.0002332935,0.0003076918,0.00006487817,0.000002853677],"category_scores_gemma":[0.0001608496,0.0001401031,0.0001645074,0.0002327919,0.00002833146,0.0009202103,0.00001262267,0.0001387896,0.000006924122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001573661,"about_ca_system_score_gemma":0.0004890336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009597158,"about_ca_topic_score_gemma":5.245669e-7,"domain_scores_codex":[0.9977693,0.0001655016,0.000851067,0.0002120386,0.0008171323,0.00018494],"domain_scores_gemma":[0.9965522,0.0001878556,0.001217894,0.0001341281,0.00174854,0.0001593736],"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":[0.0005603574,0.0005519521,0.00003090826,0.0004592098,0.00005700616,0.00006361493,0.0007300638,0.3415025,0.008323055,0.0001902508,0.0009411094,0.6465899],"study_design_scores_gemma":[0.001070504,0.0005048678,0.000006450006,0.001187725,0.00004745525,0.000258871,0.0001542559,0.96675,0.02641003,0.003374554,0.00007588523,0.0001593732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03973265,0.0001861777,0.9588892,0.0001866458,0.0003352828,0.0002352688,0.00001066066,0.00003237701,0.0003917791],"genre_scores_gemma":[0.7791128,0.00000176623,0.2205331,0.00010059,0.0001993987,0.000006267575,0.000007505947,0.00001515037,0.00002346448],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7393801,"threshold_uncertainty_score":0.5713241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08558740047888537,"score_gpt":0.293905759405991,"score_spread":0.2083183589271057,"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."}}