{"id":"W1992171538","doi":"10.1121/1.3682040","title":"Quantifying time-varying coordination of multimodal speech signals using correlation map analysis","year":2012,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Correlation; Computer science; Offset (computer science); Range (aeronautics); Correlation coefficient; Correlation function (quantum field theory); Cutoff; Algorithm; Spatial correlation; Set (abstract data type); Visualization; Filter (signal processing); Pattern recognition (psychology); Artificial intelligence; Mathematics; Computer vision; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001186704,0.0008555211,0.0005546422,0.003433487,0.0005469178,0.001660063,0.000590344,0.0005120256,0.00207369],"category_scores_gemma":[0.006394831,0.0003457858,0.0005543214,0.00272899,0.0006815058,0.001245255,0.000966746,0.0007043566,0.0006103163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000571439,"about_ca_system_score_gemma":0.001031722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003653658,"about_ca_topic_score_gemma":0.003465829,"domain_scores_codex":[0.9991406,0.0002008251,0.00003944453,0.0002033901,0.0003364473,0.00007923813],"domain_scores_gemma":[0.9979348,0.001237884,0.0002441944,0.0001579348,0.0003467904,0.00007842461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004708628,0.0001442767,0.01504859,0.000310602,0.0002484325,0.0004648511,0.0007988893,0.176833,0.0991801,0.02086356,0.003293213,0.6823437],"study_design_scores_gemma":[0.00001781221,0.000130919,0.01697394,0.00003222463,0.00006407508,0.0005494121,0.0002058962,0.936386,0.03069084,0.01064163,0.004195267,0.0001119577],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04522397,0.0001425818,0.9518688,0.00007001231,0.00002586119,0.00005336083,0.0001641394,0.0008135869,0.001637636],"genre_scores_gemma":[0.4109925,0.0002980396,0.5868828,0.00003640661,0.00004565106,0.0001262988,0.0003027969,0.0002158167,0.001099673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003653658,"threshold_uncertainty_score":0.007264733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0287061963559366,"score_gpt":0.2933105745731559,"score_spread":0.2646043782172193,"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."}}