{"id":"W3126963415","doi":"10.1109/imitec50163.2020.9334138","title":"Emotional Speaker Recognition based on Machine and Deep Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Speech recognition; Artificial intelligence; Speaker recognition; Support vector machine; Convolutional neural network; Multilayer perceptron; Random forest; Deep learning; Artificial neural network; Perceptron; Speaker diarisation; Feature extraction; Machine learning; Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000539229,0.0000522759,0.00004656324,0.00002550791,0.00008144163,0.0001011435,0.00008128811,0.0000166802,0.0001640443],"category_scores_gemma":[0.00005836028,0.00004435736,0.00001403801,0.0001242283,0.000009177189,0.0001761586,0.00003310051,0.00009207749,0.00009949483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000512695,"about_ca_system_score_gemma":0.00001121012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001518692,"about_ca_topic_score_gemma":0.000001086686,"domain_scores_codex":[0.9995327,0.00001976328,0.00006006755,0.0001845045,0.0001172671,0.00008569852],"domain_scores_gemma":[0.9998057,0.00003462408,0.00002249784,0.00004370598,0.00002167741,0.00007177145],"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":[0.00001044958,0.00002002367,0.002493448,0.00001554492,0.000002732442,0.00001133505,0.000149612,0.0004883166,0.001739714,0.0001766611,0.0001534401,0.9947387],"study_design_scores_gemma":[0.0004352848,0.0001633865,0.004954373,0.00002360739,0.000001978237,0.000006873062,0.00001341811,0.9656392,0.0259154,0.0008684393,0.001845733,0.0001323104],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01718896,0.00003704675,0.9617862,0.008098903,0.0000369078,0.00003141606,3.587291e-7,0.000169203,0.01265101],"genre_scores_gemma":[0.8511307,0.000003810469,0.1417879,0.006920594,0.00006996189,9.236902e-7,0.00000636663,0.000003850184,0.00007588194],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9946064,"threshold_uncertainty_score":0.1808841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02194544145773724,"score_gpt":0.2189283346101155,"score_spread":0.1969828931523783,"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."}}