{"id":"W3090132571","doi":"10.3233/ida-194747","title":"Recognition of speech emotion using custom 2D-convolution neural network deep learning algorithm","year":2020,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Artificial neural network; Feature extraction; Multilayer perceptron; Curse of dimensionality; Salient; Convolutional neural network; Speech recognition; Field (mathematics); Feature (linguistics); Machine learning; Perceptron; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005321001,0.0001944166,0.0004011774,0.0003148777,0.0001342916,0.00004139636,0.0003261383,0.0001693375,0.003378253],"category_scores_gemma":[0.0001416411,0.0002078196,0.0002531872,0.001817781,0.00006260725,0.0003082655,0.0001472769,0.000332278,0.0004472075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004883227,"about_ca_system_score_gemma":0.00001451739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004738729,"about_ca_topic_score_gemma":0.0000814325,"domain_scores_codex":[0.9976192,0.0004654806,0.0006626841,0.0006512377,0.0002891561,0.0003122228],"domain_scores_gemma":[0.9986148,0.00008564784,0.0004323273,0.000496957,0.0002260077,0.0001442548],"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":[0.00006889752,0.0001064521,0.004522313,0.00002188848,0.001474617,0.00001566864,0.000885323,0.01364613,0.0002924853,0.00002608057,0.0005698795,0.9783702],"study_design_scores_gemma":[0.0002797438,0.000136599,0.002289603,0.00002944611,0.003209439,0.00001248213,0.002032896,0.9881254,0.0006700077,0.0001861871,0.002744929,0.0002832089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1119769,0.0005206916,0.8852608,0.0002537777,0.0005843039,0.0002228453,0.0002234843,0.0001270772,0.0008302166],"genre_scores_gemma":[0.9670131,0.0002987806,0.01781175,0.0003565542,0.001111687,0.000005679805,0.01325228,0.00003725733,0.0001128971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9780871,"threshold_uncertainty_score":0.9975328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1403660009736294,"score_gpt":0.3535415836457495,"score_spread":0.2131755826721201,"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."}}