{"id":"W2944458161","doi":"10.3390/e21050479","title":"3D CNN-Based Speech Emotion Recognition Using K-Means Clustering and Spectrograms","year":2019,"lang":"en","type":"article","venue":"Entropy","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":155,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Spectrogram; Computer science; Speech recognition; Cluster analysis; Artificial intelligence; Preprocessor; Mel-frequency cepstrum; Pattern recognition (psychology); Convolutional neural network; SIGNAL (programming language); 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":[0.0002732365,0.0008770988,0.0004880797,0.0007750106,0.0002823418,0.0005068173,0.0006678699,0.0004824265,0.001621324],"category_scores_gemma":[0.0005879463,0.0003221891,0.0009229726,0.0004603739,0.0002338761,0.0004659274,0.0005312653,0.0003431011,0.0007797397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008745743,"about_ca_system_score_gemma":0.0004628508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01444762,"about_ca_topic_score_gemma":0.01532293,"domain_scores_codex":[0.9997638,0.00002489686,0.00001208934,0.00009096113,0.00006368692,0.00004465811],"domain_scores_gemma":[0.9998285,0.00002406968,0.00002135777,0.0000204701,0.00009347979,0.00001223901],"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":[0.000609944,0.0002034634,0.005662987,0.0001340203,0.0002606634,0.000198221,0.0002173179,0.175694,0.1416544,0.001643083,0.006920076,0.6668019],"study_design_scores_gemma":[0.000004842354,0.00003125954,0.003073758,0.000006203805,0.00002118938,0.00004069057,0.00002378297,0.9821624,0.0136184,0.0004754855,0.0005293665,0.00001248305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2048512,0.0006205534,0.7829289,0.0002380652,0.0001985145,0.0001624807,0.0008548884,0.005272594,0.004872757],"genre_scores_gemma":[0.7681983,0.000376324,0.2247813,0.0001387281,0.00004826605,0.0001395139,0.001728178,0.0001516332,0.004437833],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01444762,"threshold_uncertainty_score":0.02872705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03641278431305226,"score_gpt":0.2962719814800268,"score_spread":0.2598591971669745,"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."}}