{"id":"W2115560088","doi":"10.1109/tbme.2006.889191","title":"Acoustic Analysis and Detection of Hypernasality Using a Group Delay Function","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"All India Institute of Speech and Hearing","keywords":"Formant; Speech recognition; Acoustics; Vowel; Measure (data warehouse); Speech processing; Computer science; Physics","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":[0.0003809644,0.0006822753,0.0004141118,0.001396126,0.0002434509,0.0004945747,0.0004334575,0.0005178457,0.00203251],"category_scores_gemma":[0.001016625,0.0001777551,0.0003770158,0.0005779645,0.0004235592,0.0005901013,0.0003653787,0.0003834751,0.000811994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002250963,"about_ca_system_score_gemma":0.0002892929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004782683,"about_ca_topic_score_gemma":0.0006823682,"domain_scores_codex":[0.9996704,0.00005514094,0.00001715147,0.00007756847,0.0001529903,0.00002678078],"domain_scores_gemma":[0.9995053,0.0002443596,0.00007703281,0.00004543619,0.00009712142,0.00003071019],"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":[0.0002788668,0.00006694028,0.002169033,0.000199749,0.00004929436,0.0001702787,0.0001469239,0.002070416,0.8709704,0.0005838838,0.0001610132,0.1231332],"study_design_scores_gemma":[0.00009194299,0.001373949,0.05145486,0.00005811022,0.0002186225,0.00275748,0.0004327437,0.1342879,0.797561,0.001560593,0.01004597,0.000156795],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.327496,0.0009658387,0.6677059,0.00008864394,0.0001171574,0.0001386656,0.0002014267,0.0008342328,0.002452044],"genre_scores_gemma":[0.5878801,0.0009200701,0.4084141,0.00007860854,0.00008155416,0.0001597717,0.0002909053,0.0001301003,0.002044643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00203251,"threshold_uncertainty_score":0.006799459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008955212916931557,"score_gpt":0.2225912738224138,"score_spread":0.2136360609054823,"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."}}