{"id":"W1995193848","doi":"10.1016/j.csl.2012.11.001","title":"Adjusting dysarthric speech signals to be more intelligible","year":2012,"lang":"en","type":"article","venue":"Computer Speech & Language","topic":"Phonetics and Phonology Research","field":"Psychology","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Speech recognition; Artificial intelligence","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":[0.0002987048,0.000426176,0.0001804953,0.000231991,0.0001308524,0.0007216309,0.0002727417,0.0005138786,0.006293369],"category_scores_gemma":[0.003688403,0.0001483016,0.0001611759,0.0001283196,0.0001463997,0.000393754,0.0003445864,0.0003765163,0.00152766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001218911,"about_ca_system_score_gemma":0.0001429996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004234962,"about_ca_topic_score_gemma":0.0004023707,"domain_scores_codex":[0.999791,0.00003077592,0.00002966841,0.0000893988,0.00003563154,0.00002356462],"domain_scores_gemma":[0.9994068,0.0002590269,0.00007965297,0.00007665976,0.0001248761,0.00005298361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007738983,0.0001583693,0.001956314,0.0001059439,0.00004271666,0.00007189971,0.0001867897,0.0008824065,0.9602126,0.0003418651,0.0003165755,0.03495071],"study_design_scores_gemma":[0.001070563,0.003792293,0.1496,0.0001065354,0.0006324799,0.001297587,0.001089987,0.02427721,0.7995072,0.00365615,0.01479976,0.000170175],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9696521,0.0001707189,0.02442559,0.0002663411,0.0001980177,0.0001106525,0.0002357426,0.0007042447,0.004236552],"genre_scores_gemma":[0.9840634,0.0001376639,0.01253392,0.0002649423,0.0000391862,0.00006105841,0.0001867118,0.0002567123,0.002456425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006293369,"threshold_uncertainty_score":0.02105337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0539166861060433,"score_gpt":0.3787507243040557,"score_spread":0.3248340381980124,"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."}}