{"id":"W1492487460","doi":"10.1109/ccece.2015.7129460","title":"Automatic emotion recognition using auditory and prosodic indicative features","year":2015,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; Université de Moncton","funders":"","keywords":"Speech recognition; Computer science; Prosody; Linear discriminant analysis; Artificial intelligence; Principal component analysis; Support vector machine; Feature extraction; Emotion classification; Classifier (UML); Pattern recognition (psychology); Mel-frequency cepstrum; Natural language processing","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":[0.0006976454,0.0005774332,0.0005685737,0.000829518,0.0001867603,0.0006157608,0.0005228093,0.000499021,0.0009534127],"category_scores_gemma":[0.001574434,0.0001936602,0.000520898,0.0003281751,0.0002249097,0.0008050059,0.0004706183,0.0005588953,0.0006842177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001354599,"about_ca_system_score_gemma":0.0001884859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003441969,"about_ca_topic_score_gemma":0.0005011901,"domain_scores_codex":[0.9994711,0.0001090718,0.00003662555,0.0001341584,0.0001957698,0.00005333252],"domain_scores_gemma":[0.9995704,0.0001424091,0.00004981375,0.0000394105,0.0001748928,0.00002304387],"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":[0.0005146145,0.0001215371,0.002272067,0.0002211071,0.00006290864,0.000163879,0.0001423004,0.006305721,0.4010188,0.001472,0.002020514,0.5856845],"study_design_scores_gemma":[0.00007430201,0.0006422682,0.03623686,0.00008330854,0.000171393,0.001099988,0.0002508984,0.7172202,0.2307357,0.003813172,0.00953375,0.0001382042],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06801119,0.000634993,0.9279596,0.00009052917,0.0001348791,0.0001057492,0.000205472,0.001397767,0.001459868],"genre_scores_gemma":[0.6209294,0.000557504,0.374515,0.00009503873,0.0001311932,0.0002120127,0.0007759991,0.0001237073,0.002660248],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0009534127,"threshold_uncertainty_score":0.003689528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05814218019338874,"score_gpt":0.2732250459583764,"score_spread":0.2150828657649876,"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."}}