{"id":"W2038598237","doi":"10.1109/asru.2013.6707717","title":"Cross-lingual context sharing and parameter-tying for multi-lingual speech recognition","year":2013,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Speech recognition; Context (archaeology); Tying; Natural language processing; Artificial intelligence; Subspace topology; Covariance; Language model; Task (project management); Dialog box","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":[0.000365464,0.0001711663,0.0001979701,0.0001252419,0.0002254351,0.0009646366,0.0003451735,0.0001034542,0.0003995042],"category_scores_gemma":[0.000618582,0.0001558233,0.00008881694,0.0001236369,0.00005811612,0.0009402452,0.0001632834,0.0001081649,0.0002956465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002409145,"about_ca_system_score_gemma":0.00002571523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000207898,"about_ca_topic_score_gemma":0.00005270082,"domain_scores_codex":[0.9985946,0.00002608012,0.0003220051,0.0005543923,0.0001538532,0.0003490819],"domain_scores_gemma":[0.9988335,0.000379994,0.00009186152,0.000264271,0.0002836116,0.0001467074],"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":[0.000004810771,0.00004267713,0.001361509,0.00001694969,0.00001493147,0.000003566164,0.0002522573,2.283475e-7,0.0008700078,0.0001771637,0.00007546796,0.9971804],"study_design_scores_gemma":[0.003465629,0.0002527341,0.005954582,0.000152925,0.0000286515,0.0002025928,0.001021659,0.5839849,0.382996,0.01941846,0.001409639,0.00111224],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8207644,0.00004566192,0.1768491,0.0001586009,0.0002949225,0.0005004421,0.000005589589,0.0002556737,0.00112561],"genre_scores_gemma":[0.5211993,0.00001040864,0.4767871,0.0007507274,0.00009233097,0.00009367736,0.000007337812,0.00001513143,0.001043969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9960682,"threshold_uncertainty_score":0.9302014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1231229746230572,"score_gpt":0.3351410842406935,"score_spread":0.2120181096176363,"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."}}