{"id":"W3037952526","doi":"10.18653/v1/2020.sigmorphon-1.26","title":"Representation Learning for Discovering Phonemic Tone Contours","year":2020,"lang":"en","type":"article","venue":"","topic":"Phonetics and Phonology Research","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; Vector Institute; University of Toronto","funders":"Canadian Institute for Advanced Research; University of Pennsylvania","keywords":"Autoencoder; Mandarin Chinese; Tone (literature); Computer science; Syllable; Representation (politics); Speech recognition; Artificial intelligence; Pitch contour; Feature learning; Feature (linguistics); Natural language processing; Pattern recognition (psychology); Deep learning; Linguistics","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":[0.0008253626,0.0005636726,0.0005119201,0.0007921706,0.0003664295,0.0007420509,0.0010224,0.0008099399,0.001173876],"category_scores_gemma":[0.002831417,0.0003144685,0.0006400083,0.0006961345,0.0005403913,0.001212233,0.0008765796,0.001297555,0.0003697521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006326598,"about_ca_system_score_gemma":0.0005168949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003364775,"about_ca_topic_score_gemma":0.00316555,"domain_scores_codex":[0.9995295,0.0001448277,0.00002201961,0.0001852358,0.0000590544,0.00005931081],"domain_scores_gemma":[0.9986417,0.000802081,0.0001146174,0.0001936818,0.0002094124,0.00003856331],"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":[0.0002824462,0.0002078312,0.006754344,0.0001493549,0.0001796026,0.0001456092,0.0004114159,0.3168409,0.03763834,0.01701126,0.00365494,0.616724],"study_design_scores_gemma":[0.000007537096,0.0000241677,0.0006326771,0.000005575864,0.000009668644,0.00001985881,0.00002373566,0.9883438,0.002601136,0.008027901,0.0002967295,0.000007119461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08447055,0.0003040961,0.9127489,0.0002453257,0.00002613752,0.00003202302,0.0001547922,0.001054293,0.0009638944],"genre_scores_gemma":[0.8239861,0.000172038,0.1729252,0.0001463395,0.00004329146,0.00007817905,0.000919444,0.0001074197,0.001622049],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003364775,"threshold_uncertainty_score":0.006690383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1033771329064604,"score_gpt":0.4395076092973941,"score_spread":0.3361304763909337,"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."}}