{"id":"W2887729140","doi":"10.5220/0006855805600564","title":"Stress Detection Through Speech Analysis","year":2018,"lang":"en","type":"article","venue":"Proceedings of the 15th International Joint Conference on e-Business and Telecommunications","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mel-frequency cepstrum; Computer science; Speech recognition; Stress (linguistics); Artificial intelligence; Emotional stress; Support vector machine; Artificial neural network; Natural language processing; 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":[0.0003672013,0.0007376092,0.0004843359,0.001338981,0.0002076016,0.0008169825,0.0002719359,0.0005109052,0.001658988],"category_scores_gemma":[0.0009927469,0.0001419837,0.0003962376,0.0005250651,0.000175821,0.0004929721,0.000438404,0.0002956579,0.001648579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001342698,"about_ca_system_score_gemma":0.0001481015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007351192,"about_ca_topic_score_gemma":0.0007445066,"domain_scores_codex":[0.9996038,0.000105956,0.00002668279,0.0001204534,0.0001078165,0.00003522289],"domain_scores_gemma":[0.9995345,0.0002038049,0.00005730057,0.00003773201,0.0001404232,0.0000262431],"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":[0.0009865421,0.0001986519,0.01075026,0.0005528047,0.0001603844,0.0004746873,0.0004733762,0.004263724,0.3004699,0.00065769,0.002354394,0.6786577],"study_design_scores_gemma":[0.0001470232,0.002294889,0.2433149,0.0004284839,0.0008178066,0.003558419,0.002585938,0.3306658,0.3800952,0.006528772,0.02926914,0.0002935894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5372888,0.004932853,0.4372331,0.0004011299,0.00054438,0.0003013193,0.002394246,0.002551935,0.01435228],"genre_scores_gemma":[0.8933537,0.002552181,0.09583695,0.0001774705,0.0003473258,0.000151325,0.001767725,0.0001013323,0.005712072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001658988,"threshold_uncertainty_score":0.005549848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07020508846651731,"score_gpt":0.3315331360938772,"score_spread":0.2613280476273599,"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."}}