{"id":"W4250527430","doi":"10.1121/1.4800397","title":"Quality of voices processed by hearing aids: Intra-talker differences","year":2013,"lang":"en","type":"article","venue":"Proceedings of meetings on acoustics","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"QUIET; Speech recognition; Noise (video); Hearing aid; Perception; Speech perception; Computer science; Speech processing; Quality (philosophy); Sound quality; Acoustics; Audiology; Psychology; Artificial intelligence; Physics; Medicine","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":[0.001261686,0.0002887242,0.0003419233,0.0006287714,0.0001752778,0.0007857768,0.0001997061,0.0003600445,0.001830705],"category_scores_gemma":[0.009961048,0.0001489377,0.0002404158,0.0002960624,0.0004396799,0.0006236031,0.0006034764,0.000298815,0.00034907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001107558,"about_ca_system_score_gemma":0.00007459477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004480629,"about_ca_topic_score_gemma":0.0005368052,"domain_scores_codex":[0.9989209,0.0003104469,0.0001039018,0.0001920584,0.0004124372,0.00006014277],"domain_scores_gemma":[0.9934725,0.004423811,0.0005736763,0.0004482551,0.0008846369,0.0001971293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004113725,0.0001886411,0.07242037,0.0003562736,0.0003828427,0.0004185173,0.003549522,0.001629195,0.8546923,0.0001410695,0.0001752477,0.06193221],"study_design_scores_gemma":[0.00004279167,0.001375587,0.9205993,0.00001896133,0.0002049721,0.0008196483,0.001055367,0.003065138,0.07196293,0.0002691428,0.0005272431,0.00005892514],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957013,0.0001787578,0.003077663,0.00001168378,0.000008972126,0.0000218557,0.00009099188,0.00002574035,0.0008831056],"genre_scores_gemma":[0.9979066,0.00006342444,0.001446703,0.00001851152,0.00001142234,0.00001747949,0.0001667667,0.00003381259,0.0003353457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001830705,"threshold_uncertainty_score":0.006672502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02032917548813413,"score_gpt":0.2588673245559597,"score_spread":0.2385381490678256,"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."}}