{"id":"W4296990086","doi":"10.32604/cmc.2023.028631","title":"Multilayer Neural Network Based Speech Emotion Recognition for燬mart燗ssistance","year":2022,"lang":"en","type":"article","venue":"Computers, materials & continua/Computers, materials & continua (Print)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Majmaah University","keywords":"Computer science; Speech recognition; Buzzer; Word error rate; Surprise; Sadness; TIMIT; Biometrics; Lifelog; Artificial neural network; Artificial intelligence; Database; Hidden Markov model; Anger; Human–computer interaction","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":[0.0004474931,0.000846358,0.0005340131,0.0004881328,0.0002497456,0.000581282,0.0006571931,0.000599595,0.002645025],"category_scores_gemma":[0.000820297,0.0002256884,0.0007339805,0.0003003345,0.0001381946,0.0006698559,0.0004721356,0.0007553221,0.001378878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005046625,"about_ca_system_score_gemma":0.0003354398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007468836,"about_ca_topic_score_gemma":0.007385305,"domain_scores_codex":[0.9995876,0.00005339854,0.00003937091,0.0001306828,0.0001192817,0.00006969558],"domain_scores_gemma":[0.9998029,0.00003469708,0.00002053618,0.00001824955,0.0001140696,0.000009630659],"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":[0.0008829486,0.0003904096,0.004896266,0.0001927155,0.0002263602,0.0002177813,0.0001023763,0.05475437,0.05391137,0.000617551,0.01377377,0.8700341],"study_design_scores_gemma":[0.00001808977,0.0001582823,0.005880177,0.00002236984,0.00006963842,0.00006673714,0.00007171631,0.9722411,0.01769578,0.0005455176,0.0032092,0.00002148858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4272747,0.007670975,0.5319827,0.001492105,0.001396521,0.0002764387,0.003595797,0.008949132,0.01736149],"genre_scores_gemma":[0.9045975,0.001676593,0.07101125,0.0003664975,0.0001833104,0.0001521184,0.0055305,0.00010914,0.01637304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007468836,"threshold_uncertainty_score":0.01485074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02864722750133414,"score_gpt":0.2657589560998064,"score_spread":0.2371117285984723,"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."}}