{"id":"W2605244930","doi":"10.1002/spe.2487","title":"Deep learning and SVM‐based emotion recognition from Chinese speech for smart affective services","year":2017,"lang":"en","type":"article","venue":"Software Practice and Experience","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"Natural Science Foundation of Shandong Province; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Support vector machine; Deep belief network; Computer science; Artificial intelligence; Mel-frequency cepstrum; Sadness; Surprise; Anger; Feature (linguistics); Speech recognition; Emotion recognition; Emotion classification; Cepstrum; Machine learning; Formant; Pattern recognition (psychology); Deep learning; Feature extraction; Psychology","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":[0.0005047089,0.0003492383,0.0002902464,0.0004502143,0.0002004291,0.0003362742,0.0002386402,0.0002697563,0.00150659],"category_scores_gemma":[0.001083938,0.0001133406,0.0004111485,0.0002968133,0.0001345633,0.0003496137,0.0003931524,0.0004063303,0.0004799336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003245509,"about_ca_system_score_gemma":0.0003164541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00498283,"about_ca_topic_score_gemma":0.003616621,"domain_scores_codex":[0.9997581,0.00005183718,0.00002288334,0.0000535336,0.0000701917,0.0000434279],"domain_scores_gemma":[0.9997376,0.0000831236,0.00002110231,0.00002132648,0.0001164397,0.00002048039],"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":[0.0009289479,0.000271431,0.01210067,0.0001646041,0.00009042321,0.0002854603,0.00032382,0.05515971,0.13528,0.001234314,0.004937858,0.7892227],"study_design_scores_gemma":[0.000009904946,0.00008808335,0.01275615,0.000007733219,0.00003187665,0.00004300441,0.00009691424,0.964789,0.02096162,0.0003603185,0.0008404283,0.00001494359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8055066,0.0006232832,0.1889098,0.0003957905,0.0001974631,0.00006495423,0.0003708764,0.0008980099,0.003033173],"genre_scores_gemma":[0.962797,0.0001809559,0.03360309,0.00004771547,0.00002608117,0.0000329235,0.0004906441,0.00002463407,0.002796957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00498283,"threshold_uncertainty_score":0.009907663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02644595577279362,"score_gpt":0.3585577752525215,"score_spread":0.3321118194797279,"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."}}