{"id":"W2039983951","doi":"10.1587/elex.1.380","title":"Evaluation of speech quality using digital watermarking","year":2004,"lang":"en","type":"article","venue":"IEICE Electronics Express","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"PESQ; Computer science; Mean opinion score; Quality (philosophy); Digital watermarking; Speech recognition; Computation; Active listening; PSQM; Sound quality; Speech coding; Artificial intelligence; Linear predictive coding; Speech enhancement; Algorithm; Engineering","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":[0.001200542,0.0005479517,0.0004286908,0.001498478,0.0001651494,0.0007295298,0.000254608,0.0006329257,0.001566191],"category_scores_gemma":[0.005517803,0.0001300824,0.0003143403,0.0007958566,0.0003552499,0.0009109922,0.0005379633,0.0002311926,0.0004506061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002213368,"about_ca_system_score_gemma":0.00009549311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002689196,"about_ca_topic_score_gemma":0.0002613233,"domain_scores_codex":[0.9986749,0.0003387491,0.0001029158,0.0001379031,0.0007024886,0.00004304978],"domain_scores_gemma":[0.9975749,0.0009691383,0.0003095877,0.0002057663,0.0008686687,0.00007196686],"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":[0.001480302,0.0001408609,0.008970942,0.0006825599,0.0001650082,0.0002712228,0.0001777663,0.01154302,0.4983247,0.0009032215,0.0005945688,0.4767457],"study_design_scores_gemma":[0.000157762,0.003469578,0.04769911,0.0001035696,0.0003641563,0.001725036,0.0002261995,0.2178226,0.7223707,0.001780126,0.004104959,0.0001762907],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5515614,0.003081916,0.4372686,0.0002137722,0.000224403,0.0002099185,0.0003343964,0.001237118,0.005868443],"genre_scores_gemma":[0.906704,0.001299044,0.08919983,0.00004554417,0.0001019882,0.00004771027,0.0002633938,0.00008215811,0.002256338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001566191,"threshold_uncertainty_score":0.006349146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04516181992092887,"score_gpt":0.3298324417015992,"score_spread":0.2846706217806703,"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."}}