{"id":"W2940842789","doi":"10.1121/1.5101632","title":"Automated Mandarin tone classification using deep neural networks trained on a large speech dataset","year":2019,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Music and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mandarin Chinese; Computer science; Tone (literature); Speech recognition; Intonation (linguistics); Mel-frequency cepstrum; Pitch contour; Convolutional neural network; Artificial intelligence; Artificial neural network; Cepstrum; Pattern recognition (psychology); Feature extraction; Linguistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007763355,0.0001204608,0.0002536522,0.00001652146,0.0002168155,0.00006407614,0.001189781,0.00005865031,0.00002954048],"category_scores_gemma":[0.00005960787,0.0000636838,0.0001804584,0.0003941207,0.0001933629,0.0002073064,0.0002550915,0.0004304493,0.000003445686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005696575,"about_ca_system_score_gemma":0.00006381631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001000299,"about_ca_topic_score_gemma":1.664744e-7,"domain_scores_codex":[0.9985955,0.0001600715,0.0003761812,0.000124825,0.0004802211,0.0002632356],"domain_scores_gemma":[0.9984891,0.0002707952,0.0006104877,0.0004583433,0.0001064492,0.00006480064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003653315,0.0007439494,0.0003440894,0.0001422262,0.0003947177,0.000008713644,0.00326763,0.6193722,0.06631178,0.0001876528,0.1186809,0.1901808],"study_design_scores_gemma":[0.0003565646,0.0001210242,0.001003985,0.00004964285,0.00005814732,0.00006434369,0.0002485609,0.9971358,0.0002066231,0.0001252957,0.0005514958,0.00007857202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03070895,0.00006822266,0.9637939,0.00497351,0.0002682027,0.0001024318,0.0000139001,0.00002869748,0.00004215939],"genre_scores_gemma":[0.9266601,0.0000228397,0.06828734,0.004867721,0.0001350843,2.441227e-7,0.000003274145,0.000008850981,0.00001450791],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8959512,"threshold_uncertainty_score":0.259695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02263625642710846,"score_gpt":0.2941247880298188,"score_spread":0.2714885316027103,"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."}}