{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001343939,0.001830441,0.001135432,0.0006662551,0.000618174,0.001149441,0.001792905,0.001663001,0.007618682],"category_scores_gemma":[0.006228861,0.0006905007,0.001139043,0.0005819105,0.0004364251,0.002846769,0.00170071,0.003750701,0.007654598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006930587,"about_ca_system_score_gemma":0.001533993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006913005,"about_ca_topic_score_gemma":0.01486296,"domain_scores_codex":[0.9989257,0.0003420095,0.00006807534,0.000404703,0.0001519155,0.0001075104],"domain_scores_gemma":[0.9967324,0.001686926,0.00008727845,0.0007663233,0.0006204804,0.000106639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007499176,0.0006322336,0.003983825,0.0005195258,0.0002966038,0.0003206223,0.0009350733,0.1419318,0.06253775,0.004029354,0.04890985,0.7351536],"study_design_scores_gemma":[0.00004008886,0.0001678481,0.001373584,0.00002952817,0.00005018994,0.0001193166,0.0002059953,0.9430687,0.04232467,0.00437691,0.008200499,0.00004266696],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04487442,0.0006424041,0.9135017,0.0003317524,0.0002337915,0.0002378939,0.003121294,0.03388293,0.003173731],"genre_scores_gemma":[0.4754629,0.0004207677,0.4895579,0.00048295,0.0001481618,0.00113054,0.02117958,0.002064995,0.0095523],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007618682,"threshold_uncertainty_score":0.02548707,"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."}}