{"id":"W2121226132","doi":"10.1109/imtc.2005.1604213","title":"Speech Quality Evaluation: A New Application of Digital Watermarking","year":2006,"lang":"en","type":"article","venue":"2005 IEEE Instrumentationand Measurement Technology Conference Proceedings","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"PESQ; Computer science; Mean opinion score; Quality (philosophy); Speech recognition; Digital watermarking; Sound quality; Speech coding; PSQM; The Internet; Voice over IP; Active listening; Linear predictive coding; Artificial intelligence; Speech enhancement","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":[0.001330483,0.0008887707,0.0007624545,0.00188155,0.0002530311,0.001488431,0.0005407162,0.001156101,0.002301432],"category_scores_gemma":[0.004097191,0.0002403386,0.0003743769,0.001855609,0.001199136,0.002148236,0.0008508734,0.0008969787,0.0008334292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003825139,"about_ca_system_score_gemma":0.0001890424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002361576,"about_ca_topic_score_gemma":0.0001850529,"domain_scores_codex":[0.9981127,0.000338746,0.0001180811,0.0003176103,0.001076167,0.00003667106],"domain_scores_gemma":[0.997632,0.000968475,0.0003426984,0.0003160496,0.0006738708,0.0000668307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002473042,0.00007726259,0.001775144,0.0007725231,0.0001012087,0.000234489,0.0002346727,0.007262837,0.1016111,0.018574,0.003640232,0.8654693],"study_design_scores_gemma":[0.0001901364,0.002512377,0.01597507,0.0006494741,0.0005482134,0.00791108,0.0004775391,0.3952384,0.3163318,0.09496883,0.1647522,0.0004447789],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01947048,0.0257481,0.9339382,0.001223954,0.0007083836,0.0001332811,0.0001496401,0.001095717,0.01753221],"genre_scores_gemma":[0.4860645,0.0247684,0.4734619,0.0006261768,0.002239784,0.0001318757,0.0002128685,0.0002158966,0.01227861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002301432,"threshold_uncertainty_score":0.007699013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04711391722696232,"score_gpt":0.2910172044355185,"score_spread":0.2439032872085562,"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."}}