{"id":"W2076423492","doi":"10.1109/iscslp.2014.6936696","title":"Acoustic emotion recognition based on fusion of multiple feature-dependent deep Boltzmann machines","year":2014,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Boltzmann machine; Support vector machine; Computer science; Artificial intelligence; Restricted Boltzmann machine; Pattern recognition (psychology); Binary classification; Feature (linguistics); Feature extraction; Task (project management); Set (abstract data type); Data set; Speech recognition; Deep learning; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003317719,0.0001351376,0.0001554954,0.0001046697,0.0001072465,0.00005710061,0.0002534158,0.00006827802,0.00006341099],"category_scores_gemma":[0.0002101763,0.0001050122,0.00007532015,0.0001662214,0.00002040704,0.0002198218,0.00006491593,0.0000871526,0.00004771827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002045465,"about_ca_system_score_gemma":0.00001230067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000633006,"about_ca_topic_score_gemma":0.0001144099,"domain_scores_codex":[0.9989125,0.0001819626,0.0001643031,0.0003131873,0.000264383,0.0001637374],"domain_scores_gemma":[0.9991184,0.0002424113,0.0001056385,0.0003442386,0.0001310643,0.00005818801],"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":[0.00007689363,0.0003331192,0.0007949609,0.00003966863,0.00001761135,0.000002663337,0.0001503869,0.2706224,0.07085074,0.0002983908,0.001626371,0.6551868],"study_design_scores_gemma":[0.0004356267,0.0001903017,0.003640484,0.00003103067,0.000009517692,9.872641e-7,0.000007747221,0.9637084,0.03127899,0.0004617844,0.0001086593,0.0001264913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00797654,0.00001041214,0.9881008,0.0005237165,0.0004146487,0.0001483987,0.000002598882,0.00008237945,0.002740557],"genre_scores_gemma":[0.9216764,0.000004523209,0.07757729,0.0004020573,0.0001392409,0.000005928835,0.00001407334,0.000007932564,0.0001725406],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9136999,"threshold_uncertainty_score":0.4282272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01168933551311707,"score_gpt":0.2125541630571329,"score_spread":0.2008648275440158,"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."}}