{"id":"W4362705417","doi":"10.48047/ijfans/v11/i12/203","title":"Speech Emotion Recognition","year":2023,"lang":"en","type":"article","venue":"International Journal of Food and Nutritional Sciences","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Speech recognition; Emotion recognition; Spectrogram; Computer science; Mel-frequency cepstrum; Convolutional neural network; Classifier (UML); Multilayer perceptron; Emotion classification; Artificial intelligence; Artificial neural network; Feature extraction","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004279561,0.0009735247,0.0006869789,0.0006877247,0.0002828993,0.0009950086,0.0005654174,0.0008691669,0.01456635],"category_scores_gemma":[0.001569489,0.0001398628,0.0006785335,0.0003969262,0.0001617197,0.0008284296,0.0006228185,0.0006283874,0.01305313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002870379,"about_ca_system_score_gemma":0.0002365281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001306593,"about_ca_topic_score_gemma":0.001308802,"domain_scores_codex":[0.9994403,0.00006938742,0.00005709059,0.0001729528,0.0001783629,0.00008197627],"domain_scores_gemma":[0.999567,0.00008201216,0.00002861554,0.00005414117,0.000251932,0.0000163738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007583573,0.0001899036,0.003525379,0.0007335454,0.0001236495,0.0004699721,0.0001944655,0.004800827,0.1580635,0.002133318,0.0672657,0.7617413],"study_design_scores_gemma":[0.0001357434,0.0007159321,0.04387936,0.0003254372,0.0003241838,0.002480949,0.00102115,0.3701684,0.3643779,0.01032024,0.2060422,0.0002085451],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2205634,0.008287011,0.61077,0.001567002,0.003965304,0.001146312,0.03775677,0.02924168,0.08670252],"genre_scores_gemma":[0.6506612,0.004113205,0.2181696,0.001277018,0.0007070108,0.001017248,0.05684548,0.001158777,0.06605046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01456635,"threshold_uncertainty_score":0.04872924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08393789686073107,"score_gpt":0.3650854977447411,"score_spread":0.2811476008840101,"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."}}