{"id":"W2073701032","doi":"10.1121/1.2430765","title":"Loudness pattern-based speech quality evaluation using Bayesian modeling and Markov chain Monte Carlo methods","year":2007,"lang":"en","type":"letter","venue":"The Journal of the Acoustical Society of America","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Oticon Fonden","keywords":"Markov chain Monte Carlo; Bayesian probability; Loudness; Computer science; Monte Carlo method; Markov chain; Mathematics; Statistics; Artificial intelligence; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008945835,0.0003270398,0.0007246083,0.00006459773,0.0003432997,0.0001297788,0.001717463,0.0003431967,0.00001214876],"category_scores_gemma":[0.0004839947,0.0001905683,0.000561947,0.000486273,0.0004576908,0.0001963478,0.0003708169,0.002030642,2.342925e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000244235,"about_ca_system_score_gemma":0.0004947243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002413895,"about_ca_topic_score_gemma":8.32102e-7,"domain_scores_codex":[0.9949039,0.001612614,0.0009738115,0.000287145,0.001749551,0.0004730073],"domain_scores_gemma":[0.9955021,0.00137662,0.00161791,0.0007039434,0.000698827,0.0001006209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006106256,0.00007700672,0.00007218288,0.0004955353,0.0003204966,0.00001551994,0.001402256,0.1855309,0.005551409,1.41635e-7,0.03590857,0.770565],"study_design_scores_gemma":[0.0003337686,0.00006381026,0.00003399418,0.0003547712,0.0004021843,0.00009569474,0.0002616267,0.9956357,0.001660376,0.0005455379,0.0003826337,0.0002298771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002064157,0.0006875875,0.873169,0.1234927,0.0003740302,0.0001804432,0.000004479397,0.00001331053,0.00001426091],"genre_scores_gemma":[0.02308745,0.00007045273,0.8010259,0.1747597,0.00101178,9.170701e-7,6.624228e-7,0.0000298319,0.00001332443],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8101048,"threshold_uncertainty_score":0.8822242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05890060414016,"score_gpt":0.36774440221618,"score_spread":0.30884379807602,"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."}}