{"id":"W2143612262","doi":"10.1109/icassp.2013.6638947","title":"Speech recognition with deep recurrent neural networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":8837,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Recurrent neural network; Computer science; Connectionism; TIMIT; Speech recognition; Artificial intelligence; Deep learning; Context (archaeology); Benchmark (surveying); Time delay neural network; Artificial neural network; Hidden Markov model; Pattern recognition (psychology)","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":[0.0007586823,0.0006436461,0.0005179227,0.0005044773,0.0001809325,0.0009221419,0.0007264782,0.0007082384,0.00331165],"category_scores_gemma":[0.002312865,0.0003498581,0.0005151977,0.0005718537,0.0002620846,0.001190384,0.0007050282,0.0008824571,0.002779167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005063308,"about_ca_system_score_gemma":0.0003895355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00426619,"about_ca_topic_score_gemma":0.007310903,"domain_scores_codex":[0.9993184,0.0001493286,0.00004834553,0.0001720516,0.0002531724,0.00005869945],"domain_scores_gemma":[0.999339,0.0002444432,0.00005612234,0.0001454831,0.0001930563,0.00002175245],"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":[0.0002555691,0.0001000008,0.0009714167,0.0001858028,0.0001741563,0.0001693924,0.0000737193,0.2257454,0.06714167,0.006321207,0.007350397,0.6915113],"study_design_scores_gemma":[0.000008188331,0.00005060496,0.0004709507,0.00001497491,0.00001813379,0.00005218451,0.00001173566,0.9773127,0.01626534,0.003013343,0.002767304,0.00001450159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04053292,0.001904174,0.944698,0.0003678601,0.0001987479,0.0000498676,0.0005830534,0.006964324,0.004701076],"genre_scores_gemma":[0.6135056,0.001254581,0.370025,0.0002703624,0.000158336,0.0001085123,0.002838765,0.0002847179,0.01155421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00426619,"threshold_uncertainty_score":0.0110786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02127521644955892,"score_gpt":0.2153082989889602,"score_spread":0.1940330825394013,"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."}}