{"id":"W2396030159","doi":"10.21437/interspeech.2013-432","title":"Using an autoencoder with deformable templates to discover features for automated speech recognition","year":2013,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; TIMIT; Spectrogram; Autoencoder; Hidden Markov model; Speech recognition; Artificial neural network; Artificial intelligence; Pattern recognition (psychology); Set (abstract data type); Dropout (neural networks); Task (project management); Test set; Machine learning","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":[0.0007021584,0.0006176422,0.0004828944,0.0004804644,0.0001939274,0.0004801434,0.0006389301,0.0006972479,0.001316946],"category_scores_gemma":[0.002155263,0.0005029706,0.0005029661,0.0004643823,0.000420796,0.001226638,0.0005858651,0.001025044,0.0007904255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003998373,"about_ca_system_score_gemma":0.0003710606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002780166,"about_ca_topic_score_gemma":0.005118056,"domain_scores_codex":[0.9996552,0.00006390804,0.00002019966,0.0001223694,0.000108798,0.00002938641],"domain_scores_gemma":[0.9992779,0.0003689213,0.00006179547,0.0001657176,0.0001060026,0.00001966978],"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":[0.0001816548,0.0001144804,0.00191754,0.00009093367,0.0001188118,0.0001948429,0.0001037698,0.2072208,0.1065112,0.006234107,0.001705178,0.6756065],"study_design_scores_gemma":[0.000004722648,0.00003730195,0.0008168253,0.00000809526,0.00001219352,0.00008600639,0.000007820443,0.9714206,0.02406189,0.002127866,0.001405251,0.00001135365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02379244,0.0002032934,0.9740831,0.00008439743,0.00003635112,0.00002038104,0.00008599597,0.001037601,0.0006563765],"genre_scores_gemma":[0.3366457,0.0003588329,0.6577933,0.0001260191,0.00005062448,0.0000816242,0.0005680569,0.0002060712,0.004169722],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002780166,"threshold_uncertainty_score":0.005527914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04314113276841627,"score_gpt":0.2933091318944558,"score_spread":0.2501679991260396,"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."}}