{"id":"W2132037657","doi":"10.1109/icassp.2011.5947700","title":"Learning a better representation of speech soundwaves using restricted boltzmann machines","year":2011,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":229,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cepstrum; Speech recognition; Computer science; Mel-frequency cepstrum; Linear predictive coding; Boltzmann machine; Restricted Boltzmann machine; Representation (politics); Artificial intelligence; Speech coding; Pattern recognition (psychology); Artificial neural network; Feature extraction","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":[0.0008940009,0.0005685542,0.001037107,0.0002944562,0.0002058423,0.0008745331,0.000932127,0.001056824,0.001506037],"category_scores_gemma":[0.003733619,0.0005379779,0.000874279,0.0003112698,0.0005227129,0.002074662,0.0006683923,0.001486319,0.0006135412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004668699,"about_ca_system_score_gemma":0.0004740619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001633349,"about_ca_topic_score_gemma":0.001988678,"domain_scores_codex":[0.9996507,0.0001526049,0.00002289269,0.00008902267,0.00005119025,0.00003354511],"domain_scores_gemma":[0.999222,0.0004759167,0.00005761803,0.0001267739,0.00008960974,0.00002807551],"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":[0.0000977109,0.00007390131,0.0007083044,0.00007647688,0.00007514495,0.00005862439,0.00008421391,0.9089369,0.01457294,0.01307112,0.0007550344,0.06148966],"study_design_scores_gemma":[0.000004070934,0.00000823611,0.00004052119,0.000001865207,0.000002651707,0.000005100698,0.000002701754,0.9963164,0.0005059255,0.003023939,0.00008406675,0.000004435276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04029168,0.0002020041,0.9581484,0.0002651043,0.00004375783,0.00002254106,0.00005523728,0.0004590312,0.0005121914],"genre_scores_gemma":[0.7289907,0.0003438052,0.2665318,0.0002602578,0.00007845676,0.0001808593,0.0002836749,0.0001758049,0.003154685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001633349,"threshold_uncertainty_score":0.005038202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07598615674813156,"score_gpt":0.2849837548539779,"score_spread":0.2089975981058464,"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."}}