{"id":"W4205558880","doi":"10.31234/osf.io/t8eyh","title":"Talker variability facilitates the statistical learning of speech sounds","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Task (project management); Active listening; Statistical learning; Speech recognition; Syllable; Natural (archaeology); Psychology; Computer science; Artificial intelligence; Communication","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":[0.001201513,0.0004313765,0.000504352,0.0002670848,0.0002479415,0.0008748593,0.0005591364,0.0004775558,0.002532918],"category_scores_gemma":[0.00696811,0.0004715992,0.0003868979,0.00008730788,0.0007007605,0.0008559463,0.001517088,0.001112198,0.000469076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001618361,"about_ca_system_score_gemma":0.0003071082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003264445,"about_ca_topic_score_gemma":0.0005002999,"domain_scores_codex":[0.9987302,0.0002791155,0.00009421634,0.0004488646,0.0003441678,0.000103347],"domain_scores_gemma":[0.9944127,0.003588218,0.0006639715,0.0007140479,0.0002645184,0.0003566515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0008509099,0.000220053,0.005370601,0.000192496,0.00004707903,0.0002402479,0.001212654,0.0009310617,0.9517258,0.0003661387,0.0001459054,0.03869719],"study_design_scores_gemma":[0.0001591585,0.005618802,0.2386915,0.0001006946,0.0002648393,0.002588639,0.00105617,0.02347143,0.7176381,0.004607186,0.005625998,0.0001775014],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9828871,0.0001662911,0.01512454,0.00009248014,0.00002480576,0.00002356799,0.00004130583,0.0002133989,0.001426542],"genre_scores_gemma":[0.9908063,0.00008297717,0.008166917,0.00004279694,0.00002259035,0.00003156832,0.00006718664,0.00006076069,0.0007188196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002532918,"threshold_uncertainty_score":0.008473516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01765082392841951,"score_gpt":0.2669455784915212,"score_spread":0.2492947545631017,"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."}}