{"id":"W2180676852","doi":"10.1109/waspaa.2015.7336888","title":"PhySyQX: A database for physiological evaluation of synthesised speech quality-of-experience","year":2015,"lang":"en","type":"article","venue":"","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Ministère du Développement Économique, de l’Innovation et de l’Exportation","keywords":"Computer science; Database; Quality (philosophy); Natural language processing; Speech recognition","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001404971,0.001423015,0.001505512,0.003671926,0.0002859028,0.001213205,0.00145148,0.001411151,0.01501433],"category_scores_gemma":[0.006625089,0.0003423243,0.001032012,0.002723234,0.0002408288,0.00131162,0.001398245,0.000553492,0.008948398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000582216,"about_ca_system_score_gemma":0.0007812555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003882665,"about_ca_topic_score_gemma":0.004757321,"domain_scores_codex":[0.99858,0.0002278251,0.0004584659,0.0002791584,0.0003850737,0.00006949372],"domain_scores_gemma":[0.9960187,0.001179313,0.0006249668,0.0006787946,0.001237668,0.0002604756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.008752968,0.001143616,0.08440449,0.01659959,0.001684768,0.002090852,0.001288307,0.01173757,0.03015419,0.002628214,0.3798352,0.4596803],"study_design_scores_gemma":[0.001738631,0.002558548,0.5452807,0.001423288,0.001035352,0.003391668,0.00116888,0.03235634,0.01834222,0.005952056,0.3860429,0.0007095124],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.05336184,0.00277745,0.02135658,0.0001871662,0.0002389431,0.001573505,0.9052824,0.008007579,0.007214508],"genre_scores_gemma":[0.110683,0.001577877,0.01918151,0.0001602251,0.0001254795,0.004720347,0.8575471,0.0005728148,0.0054317],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01501433,"threshold_uncertainty_score":0.05022788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4922163225748313,"score_gpt":0.4966917256710833,"score_spread":0.004475403096252051,"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."}}