{"id":"W2594863811","doi":"10.1109/icassp.2017.7953135","title":"Biologically inspired speech emotion recognition","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Speech recognition; Feature extraction; Classifier (UML); Speech processing; Vocal tract; Voice activity detection; Artificial intelligence; Filter (signal processing); Curse of dimensionality; Speech production; Pattern recognition (psychology); Artificial neural network; Set (abstract data type); Signal processing; Digital signal processing","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":[0.0001868909,0.0001897552,0.0002606507,0.0001633533,0.0001071564,0.0004726933,0.000417855,0.0004298745,0.001404868],"category_scores_gemma":[0.0006855299,0.0001048222,0.0002966356,0.0001773536,0.0002237368,0.0004260078,0.0003622551,0.0003627211,0.0003938764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000277649,"about_ca_system_score_gemma":0.000152918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003390007,"about_ca_topic_score_gemma":0.0003539193,"domain_scores_codex":[0.9999045,0.00001837646,0.000005733351,0.000027546,0.00003243556,0.00001141965],"domain_scores_gemma":[0.9999084,0.00003201573,0.00001286653,0.00001485514,0.00002608441,0.000005682337],"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":[0.0001564951,0.00008651582,0.002112166,0.0002714692,0.00009905305,0.0001623334,0.0001745554,0.1049623,0.3672165,0.03002733,0.00534448,0.4893868],"study_design_scores_gemma":[0.00001049497,0.00005082595,0.002277066,0.00001519243,0.00002032074,0.0001472848,0.00004019629,0.916851,0.05361723,0.02002414,0.006927043,0.00001926587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1382867,0.001990593,0.8455153,0.0008167849,0.0003003975,0.00007236427,0.000397875,0.001851107,0.01076899],"genre_scores_gemma":[0.8333072,0.0007637086,0.1588255,0.0002196806,0.00006106846,0.00008619444,0.0004476889,0.00007116947,0.006217692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001404868,"threshold_uncertainty_score":0.004699826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07557748951194194,"score_gpt":0.2852938348003649,"score_spread":0.2097163452884229,"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."}}