{"id":"W2928075308","doi":"10.21437/interspeech.2019-2396","title":"Speech Model Pre-Training for End-to-End Spoken Language Understanding","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University","funders":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Computer science; End-to-end principle; Training set; Spoken language; Speech recognition; Training (meteorology); Artificial intelligence; Language model; Natural language processing; Gauge (firearms); Speech synthesis","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007332026,0.0003435957,0.0004427622,0.0002652785,0.00009508066,0.0004306056,0.00179701,0.0002815889,0.00004638912],"category_scores_gemma":[0.00007908337,0.0003358441,0.0001987371,0.0001214646,0.00001657777,0.0002386318,0.002317663,0.0004398849,0.00003352172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003486104,"about_ca_system_score_gemma":0.0003980443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001537594,"about_ca_topic_score_gemma":0.00007681858,"domain_scores_codex":[0.9973256,0.00003071682,0.0004028701,0.001185899,0.0004617631,0.0005931437],"domain_scores_gemma":[0.9980232,0.0002177034,0.0001471689,0.001386978,0.00005786061,0.0001670508],"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":[0.00001996619,0.00003014227,0.00005735776,0.0003543889,0.00009380469,0.00001571874,0.02572837,0.6809857,0.0005915884,0.2466198,0.0006907237,0.04481244],"study_design_scores_gemma":[0.0002439174,0.00002353087,0.00001115362,0.0001913005,0.00001580399,0.000005890741,0.000688781,0.9630942,0.0003808652,0.03484721,0.00008068795,0.000416613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01526148,0.00005998757,0.9613541,0.0007815908,0.0009842212,0.001114455,0.00002682077,0.0004295764,0.01998773],"genre_scores_gemma":[0.5568866,0.000002484473,0.4380521,0.0004149107,0.0001655403,0.00004201107,0.000011585,0.00002637855,0.004398327],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5416251,"threshold_uncertainty_score":0.9999093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1407860486826978,"score_gpt":0.3303666889839246,"score_spread":0.1895806403012268,"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."}}