{"id":"W2900035714","doi":"10.1080/17549507.2018.1508499","title":"Automatic prediction of intelligible speaking rate for individuals with ALS from speech acoustic and articulatory samples","year":2018,"lang":"en","type":"article","venue":"International Journal of Speech-Language Pathology","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute on Deafness and Other Communication Disorders; National Institutes of Health; American Speech-Language-Hearing Foundation","keywords":"Speech recognition; Intelligibility (philosophy); Tongue; Mean squared error; Computer science; Word error rate; Speech error; Support vector machine; Speech production; Artificial intelligence; Pattern recognition (psychology); Mathematics; Statistics; Linguistics","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":[0.0007958239,0.0007113123,0.0004223397,0.0009678957,0.0001622151,0.0005338086,0.0002211333,0.0004320796,0.0007645313],"category_scores_gemma":[0.003802398,0.0001273807,0.0002891198,0.0002016345,0.0001279275,0.0003819304,0.0002824,0.0002666886,0.0006224037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001180058,"about_ca_system_score_gemma":0.0001711089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006916433,"about_ca_topic_score_gemma":0.0009593203,"domain_scores_codex":[0.9996802,0.00008890677,0.00004604468,0.00009055245,0.00007695501,0.00001744436],"domain_scores_gemma":[0.9986854,0.0006865882,0.0002036523,0.00006629434,0.0002992502,0.00005868174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002020547,0.0003248941,0.5195853,0.0003546938,0.0002459883,0.0005264911,0.0008198433,0.01403971,0.07966024,0.0001810004,0.0009928655,0.3812485],"study_design_scores_gemma":[0.00005220141,0.001179653,0.6940262,0.00009341457,0.0002632441,0.0017968,0.0008533341,0.274826,0.02507972,0.0006488506,0.001098041,0.0000826395],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9615764,0.0003870303,0.03630812,0.00006243743,0.00003553231,0.00005392971,0.0005151803,0.0003324987,0.0007289195],"genre_scores_gemma":[0.9865012,0.0001306235,0.01243929,0.00001659665,0.00001914664,0.00003715955,0.0004735191,0.000018233,0.0003641935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009678957,"threshold_uncertainty_score":0.004208803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02020683450282267,"score_gpt":0.3090213836923736,"score_spread":0.2888145491895509,"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."}}