{"id":"W2364974426","doi":"","title":"Phoneme modeling units design for Mandarin LVCSR systems","year":2011,"lang":"en","type":"article","venue":"Journal of Tsinghua University(Science and Technology)","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Mandarin Chinese; Computer science; Set (abstract data type); Speech recognition; Salient; Vocabulary; Artificial intelligence; Vowel; Natural language processing; Linguistics; Programming language","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":[0.0004580277,0.0008191898,0.0004978717,0.0004019634,0.0003586906,0.0006290996,0.0009145518,0.0007070111,0.003384883],"category_scores_gemma":[0.001029612,0.0003831,0.0005680862,0.0001867623,0.0002215902,0.0008212356,0.0004338008,0.0007090951,0.001266204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003614613,"about_ca_system_score_gemma":0.0003903612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001183152,"about_ca_topic_score_gemma":0.001310226,"domain_scores_codex":[0.9994619,0.0001448428,0.00008560744,0.0001269799,0.0001438739,0.00003663996],"domain_scores_gemma":[0.9996485,0.0001109326,0.00003913981,0.00005491418,0.0001281014,0.00001856188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001001659,0.0001295636,0.001341135,0.0006057923,0.0001423521,0.0004597734,0.0005357608,0.1107687,0.4592338,0.01215801,0.00222308,0.4114005],"study_design_scores_gemma":[0.0001049787,0.0009874277,0.001580319,0.00005120204,0.0001603948,0.0005700521,0.0001115961,0.7199557,0.2454713,0.00382743,0.02709652,0.00008306815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02605784,0.0003938085,0.9702843,0.00005524696,0.00006151958,0.0001423829,0.0001157353,0.001279726,0.001609395],"genre_scores_gemma":[0.585874,0.0002619612,0.408934,0.00009684184,0.00006202792,0.0005583811,0.0005746591,0.0002673858,0.0033707],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003384883,"threshold_uncertainty_score":0.01132357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08201215340333769,"score_gpt":0.2142346772779408,"score_spread":0.1322225238746031,"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."}}