{"id":"W7025109027","doi":"","title":"Using Auditory Models for Speaker Normalization in Speech Recognition","year":2022,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Normalization (sociology); Speaker recognition; Speech processing; Speaker identification; Pattern recognition (psychology); Computational auditory scene analysis","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":[0.001048776,0.0007337889,0.0005773035,0.0005325274,0.0004763315,0.001229807,0.000604627,0.0007603996,0.003859098],"category_scores_gemma":[0.003942796,0.0004806059,0.0008715471,0.0004799331,0.0004162561,0.001253503,0.0008002899,0.001444759,0.00317932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004702182,"about_ca_system_score_gemma":0.001153789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008482933,"about_ca_topic_score_gemma":0.0146223,"domain_scores_codex":[0.9993531,0.0001863812,0.00003722887,0.0001649016,0.0002023942,0.00005590337],"domain_scores_gemma":[0.9990709,0.0004703996,0.00005005546,0.0001074501,0.0002707075,0.0000305114],"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":[0.0004959163,0.0001996043,0.001539197,0.000220505,0.0002276209,0.0001567613,0.0002676942,0.2762649,0.07339821,0.01206625,0.005393982,0.6297694],"study_design_scores_gemma":[0.00001399387,0.00005092342,0.0007551577,0.00002302522,0.00004849064,0.00008517647,0.00004649976,0.965688,0.02485334,0.005248404,0.003155306,0.00003182278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01270382,0.0002820169,0.9831982,0.0001437604,0.0001487611,0.00003231729,0.000144933,0.001832476,0.001513619],"genre_scores_gemma":[0.4568007,0.001085434,0.5279451,0.0002509487,0.0002059277,0.000186428,0.0009697966,0.0009543461,0.01160132],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008482933,"threshold_uncertainty_score":0.0168671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0898020178580071,"score_gpt":0.2975509914898056,"score_spread":0.2077489736317985,"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."}}