{"id":"W2567462284","doi":"10.1109/jstsp.2016.2638538","title":"Multimodal Physiological Quality-of-Experience Assessment of Text-to-Speech Systems","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère du Développement Économique, de l’Innovation et de l’Exportation","keywords":"Computer science; Neuroimaging; Electroencephalography; Perception; Active listening; Quality (philosophy); Quality of experience; Functional near-infrared spectroscopy; Brain activity and meditation; Speech recognition; Human–computer interaction; Artificial intelligence; Cognition; Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005441742,0.0001501212,0.0004933043,0.0002162189,0.00005329077,0.00004266289,0.0005319361,0.00009264776,0.0000136651],"category_scores_gemma":[0.0004060795,0.00009233687,0.00007074653,0.000578454,0.0001471221,0.000321574,0.00004919833,0.0002949563,7.677899e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008644307,"about_ca_system_score_gemma":0.0002178874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001185792,"about_ca_topic_score_gemma":0.000001423668,"domain_scores_codex":[0.997488,0.0003160948,0.001094148,0.0002460855,0.0005924995,0.0002632261],"domain_scores_gemma":[0.9979724,0.0004604679,0.0008715588,0.0001091817,0.0004987675,0.00008763554],"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":[0.00006832077,0.0002071356,0.003839373,0.0001062893,0.000005406982,0.0000165537,0.0006145264,0.001070979,0.9791659,0.0001065414,0.0000200638,0.01477893],"study_design_scores_gemma":[0.0007069238,0.0006624935,0.01674399,0.001088468,0.000005363523,0.00007277363,0.0001436026,0.004681252,0.9754146,0.0002536017,0.0000602436,0.0001666885],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9600779,0.00006918973,0.03916813,0.0001534118,0.0002653096,0.0001122335,0.000003285315,0.00001170274,0.0001388353],"genre_scores_gemma":[0.9955641,0.00001313867,0.004055111,0.00006731925,0.000236286,0.00000286175,6.145596e-8,0.000008772211,0.00005237394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03548617,"threshold_uncertainty_score":0.3765388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07240753945071553,"score_gpt":0.3737257920251675,"score_spread":0.301318252574452,"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."}}