{"id":"W2770781878","doi":"10.17077/drivingassessment.1629","title":"Voice-Controlled In-Vehicle Systems: Effects of Voice-Recognition Accuracy in the Presence of Background Noise","year":2017,"lang":"en","type":"article","venue":"","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Noise (video); Computer science; Context (archaeology); Noise measurement; Background noise; Set (abstract data type); Speech recognition; Simulation; Noise reduction; Artificial intelligence; Telecommunications","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":[0.002242705,0.0007612094,0.0004297118,0.0003212821,0.0002735602,0.001011123,0.0004048503,0.0006729093,0.001985513],"category_scores_gemma":[0.02647477,0.0004057076,0.0004093311,0.0001413109,0.0004843804,0.0008991032,0.0008347001,0.0004754689,0.0003629316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002055483,"about_ca_system_score_gemma":0.0001867124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001286753,"about_ca_topic_score_gemma":0.001787228,"domain_scores_codex":[0.9974306,0.0009018508,0.0003338727,0.0004146024,0.0007314134,0.0001876396],"domain_scores_gemma":[0.9700494,0.02159824,0.0031997,0.001468648,0.002903317,0.0007805957],"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":[0.02949655,0.003591413,0.3526406,0.001503243,0.0009556978,0.0006605795,0.01568001,0.00345869,0.4954317,0.0001319329,0.0003659759,0.09608365],"study_design_scores_gemma":[0.0001678187,0.01646901,0.9160029,0.00004826323,0.0004656728,0.000523039,0.002854023,0.003513223,0.05911399,0.0001041213,0.0006484517,0.00008947954],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989532,0.00004500749,0.0006087496,0.000008841742,0.000007558128,0.00001977988,0.00002575882,0.00001243816,0.0003186642],"genre_scores_gemma":[0.9986703,0.00004270984,0.0007878323,0.00002228961,0.000008818121,0.00002187207,0.00008572714,0.00001491577,0.0003454507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002242705,"threshold_uncertainty_score":0.01186067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04508825102554444,"score_gpt":0.3707628163135123,"score_spread":0.3256745652879678,"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."}}