{"id":"W3154935508","doi":"10.1007/s12559-021-09865-2","title":"When Old Meets New: Emotion Recognition from Speech Signals","year":2021,"lang":"en","type":"article","venue":"Cognitive Computation","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Politecnico di Milano","keywords":"Computer science; Speech recognition; Artificial intelligence; Convolutional neural network; Feature (linguistics); Spectrogram; Mel-frequency cepstrum; Support vector machine; Artificial neural network; Pattern recognition (psychology); Feature extraction; Emotion recognition","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":[0.0005272218,0.0007883718,0.000446695,0.0004666985,0.0001586739,0.0006684793,0.0004162458,0.0006611386,0.001105933],"category_scores_gemma":[0.001749737,0.0001185981,0.0003719195,0.0002797356,0.0001826468,0.0006385777,0.0004047189,0.0007733934,0.001175101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001995704,"about_ca_system_score_gemma":0.0001479524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001265177,"about_ca_topic_score_gemma":0.001220278,"domain_scores_codex":[0.9996056,0.0001001085,0.00002350054,0.0001171428,0.0001016759,0.00005204742],"domain_scores_gemma":[0.9993271,0.0003096885,0.00006338952,0.0000466695,0.0002158799,0.00003740991],"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":[0.002126155,0.0004180089,0.01797453,0.0003757102,0.0001846458,0.0008662441,0.000334052,0.05768202,0.1702322,0.0009355347,0.0174167,0.7314543],"study_design_scores_gemma":[0.00002209521,0.0002311273,0.01653703,0.0000386974,0.00006731898,0.0002048129,0.0002916052,0.9280723,0.0503404,0.00139135,0.002766781,0.00003653761],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7424287,0.00352156,0.2398676,0.001282946,0.001050662,0.0001385538,0.002369207,0.003241309,0.006099563],"genre_scores_gemma":[0.9535969,0.0006430927,0.04099523,0.0001967176,0.0002010317,0.00003913904,0.00174836,0.00005351264,0.002526032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001265177,"threshold_uncertainty_score":0.00369972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07578580399662282,"score_gpt":0.3358346409619837,"score_spread":0.2600488369653609,"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."}}