{"id":"W7062304316","doi":"","title":"Speech Intelligibility Assessment using Automatic Speech Recognition","year":2024,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Advanced Power Generation Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Audiologist; Hearing aid; Intelligibility (philosophy); Microphone; Hearing loss; Population; Key (lock)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001148259,0.0008842697,0.0005173053,0.001299419,0.0002282815,0.00152609,0.0006805106,0.0007894412,0.003963358],"category_scores_gemma":[0.00412289,0.000238481,0.0008290838,0.0004750386,0.0002840916,0.0008941453,0.0007200931,0.0005324363,0.003144939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004073703,"about_ca_system_score_gemma":0.000657675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003448069,"about_ca_topic_score_gemma":0.003634633,"domain_scores_codex":[0.9989815,0.0002028837,0.000070192,0.0002289587,0.0004677456,0.0000486801],"domain_scores_gemma":[0.9985762,0.0006473349,0.0001417473,0.00009568654,0.0005011332,0.0000378763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008934843,0.0004789469,0.02314676,0.0006698421,0.0002465718,0.00045069,0.0005373597,0.05069759,0.1323522,0.001932674,0.004528578,0.7840653],"study_design_scores_gemma":[0.0001017217,0.001881228,0.05902464,0.0002451161,0.0003234866,0.002364402,0.0006359908,0.7687653,0.149097,0.002906942,0.01435084,0.0003033643],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4625242,0.001588459,0.5066867,0.0004688188,0.0002379552,0.0006641473,0.002314623,0.006591929,0.01892319],"genre_scores_gemma":[0.8385098,0.001368536,0.1506987,0.0001972033,0.00007893989,0.0004154791,0.001786723,0.0002487542,0.006695924],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003963358,"threshold_uncertainty_score":0.01325876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1153629775530894,"score_gpt":0.3499086724078313,"score_spread":0.2345456948547419,"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."}}