{"id":"W2400346108","doi":"10.1109/icassp.2016.7472812","title":"A subjective listening test of six different artificial bandwidth extension approaches in English, Chinese, German, and Korean","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Nuance Communications (Canada)","funders":"","keywords":"Active listening; German; Computer science; Test (biology); Speech recognition; Extension (predicate logic); Natural language processing; Context (archaeology); Artificial intelligence; Psychology; Linguistics; Communication","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001809224,0.0001684327,0.0002593587,0.0001805108,0.00004410536,0.00003637905,0.000404312,0.00005886986,0.00000600263],"category_scores_gemma":[0.0004087829,0.00009171756,0.00003227963,0.00023696,0.00009220975,0.0006547145,0.0006339415,0.00009428983,0.000001006832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002815259,"about_ca_system_score_gemma":0.00001386939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003234038,"about_ca_topic_score_gemma":0.00008749932,"domain_scores_codex":[0.998766,0.00005924811,0.0003060722,0.0004706725,0.000195582,0.0002024335],"domain_scores_gemma":[0.9987352,0.0005409769,0.000121356,0.0004523507,0.00008195973,0.00006812804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009081619,0.0006910561,0.2273291,0.00005623369,0.00001364947,0.00002462377,0.003560239,0.000007046549,0.3101258,0.04220303,0.0001933082,0.4157051],"study_design_scores_gemma":[0.0007972454,0.0003187903,0.508198,0.0003515381,0.00000495104,0.00001462625,0.00008017286,0.01776376,0.4131829,0.05869186,0.0001068628,0.0004892827],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5475757,0.00004224653,0.451223,0.0001054558,0.0000478672,0.0001758884,0.00000685852,0.0002090257,0.0006139692],"genre_scores_gemma":[0.9666111,0.00001992526,0.03321645,0.00002084156,0.00003271995,0.00002035878,0.0000017159,0.000008890663,0.00006800461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4190354,"threshold_uncertainty_score":0.3740133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02618577364371445,"score_gpt":0.269213162683438,"score_spread":0.2430273890397236,"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."}}