{"id":"W2050943672","doi":"10.1121/1.4743688","title":"Gross versus detailed spectral cues in spectrally distorted speech","year":2000,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Formant; Acoustics; Noise (video); Spectral resolution; Consonant; Spectral shape analysis; Mathematics; Perception; Articulation (sociology); Place of articulation; Speech recognition; Computer science; Spectral line; Physics; Vowel; Artificial intelligence; Psychology","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":[0.0005341191,0.0001411527,0.0003081904,0.00002007875,0.0001519949,0.00005449334,0.001719985,0.00005124926,0.0001048401],"category_scores_gemma":[0.0001026934,0.00007650444,0.0003450642,0.0006148677,0.0004493865,0.0002613368,0.000126089,0.000528183,0.000009447238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000113954,"about_ca_system_score_gemma":0.000139694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003760737,"about_ca_topic_score_gemma":0.000005225303,"domain_scores_codex":[0.9983788,0.0001182379,0.0004546437,0.0001246994,0.0005700237,0.0003536157],"domain_scores_gemma":[0.9988338,0.0003260026,0.0003190521,0.0003640112,0.00008014443,0.00007702094],"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":[0.002106058,0.001348125,0.003947753,0.000112536,0.0006685111,0.00009100253,0.009351372,0.02441782,0.1277872,0.00006220827,0.03786163,0.7922458],"study_design_scores_gemma":[0.01435208,0.00482676,0.157242,0.00104657,0.0009856735,0.001625941,0.004659762,0.4854318,0.2848505,0.03163779,0.01125353,0.002087636],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6765477,0.0007503349,0.3036427,0.01662069,0.0005742482,0.0001679173,0.000002699769,0.00004271798,0.001650966],"genre_scores_gemma":[0.836808,0.0004002449,0.1615005,0.0009319113,0.0002118284,3.726048e-7,1.005629e-7,0.000009837141,0.0001372378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7901582,"threshold_uncertainty_score":0.3196189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01206534291031161,"score_gpt":0.249052524858651,"score_spread":0.2369871819483394,"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."}}