{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001423228,0.0001546005,0.0001882041,0.0001316527,0.0001122718,0.0001293393,0.0007340918,0.0000770351,0.000001599914],"category_scores_gemma":[0.00005775103,0.0001487436,0.0001000955,0.0003351314,0.00005240737,0.001127382,0.0001786043,0.0001726813,0.000001382797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002319191,"about_ca_system_score_gemma":0.0002188275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003049529,"about_ca_topic_score_gemma":0.000005166037,"domain_scores_codex":[0.9979172,0.0001315183,0.0003464279,0.000347458,0.0008629492,0.0003944672],"domain_scores_gemma":[0.9987313,0.00004275148,0.0002136907,0.0005955239,0.0003706058,0.00004618704],"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":[0.00006502228,0.0003955881,0.0009257871,0.0001106446,0.0001761813,0.00001582537,0.002760722,0.02486371,0.4941598,0.1013809,0.00002123154,0.3751247],"study_design_scores_gemma":[0.000658004,0.00008910773,0.0001149114,0.0000887047,0.00003161413,0.00002802379,0.00001609019,0.01345163,0.7074598,0.2767622,0.0009962032,0.0003037712],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4012275,0.000388934,0.5971821,0.00004230092,0.0001019291,0.0001747466,0.000003667133,0.0001679434,0.0007108634],"genre_scores_gemma":[0.9455687,0.00002117031,0.05430532,0.00002110529,0.00005382478,0.00001089916,0.000004483026,0.00001100348,0.000003528388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5443411,"threshold_uncertainty_score":0.6065587,"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."}}