{"id":"W2127108986","doi":"10.1109/iscas.2007.378460","title":"Emotion Recognition Using Novel Speech Signal Features","year":2007,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sadness; Surprise; Support vector machine; Disgust; Speech recognition; Computer science; Emotion classification; Anger; Happiness; Artificial intelligence; Pattern recognition (psychology); Emotion recognition; Classifier (UML); Speaker recognition; Human voice; Set (abstract data type); Psychology; Communication","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004587205,0.0001122042,0.000094663,0.0001934793,0.00009385259,0.00002369883,0.00004908017,0.0001880843,0.01084256],"category_scores_gemma":[0.00001735011,0.0001066207,0.00007101311,0.000193634,0.00003061166,0.0001118616,0.00001152709,0.0001686919,0.0009051528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004445775,"about_ca_system_score_gemma":0.00001063629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001704909,"about_ca_topic_score_gemma":0.00009677819,"domain_scores_codex":[0.9990933,0.00003625953,0.000217138,0.0002405015,0.0001473248,0.0002654841],"domain_scores_gemma":[0.9995613,0.00004997997,0.00007774172,0.0001100701,0.0001175863,0.00008332911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002076385,0.0007239357,0.0006306484,0.00001622176,0.00007565215,0.0000297723,0.0008212133,0.000003773608,0.1023165,0.003029017,0.005678182,0.8864674],"study_design_scores_gemma":[0.01709,0.001600765,0.520369,0.0004948438,0.0005682915,0.006788298,0.01998815,0.0009718525,0.3732972,0.02785696,0.02749056,0.003484095],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.492256,0.00002389224,0.2005614,0.0001182671,0.00118791,0.0001919119,0.000009963212,0.0001724611,0.3054781],"genre_scores_gemma":[0.9683549,0.000002596439,0.02439367,0.0009648692,0.0006358694,0.000002502349,0.0001286615,0.00002349162,0.005493378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8829833,"threshold_uncertainty_score":0.9998727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08841240013290458,"score_gpt":0.3516560660787058,"score_spread":0.2632436659458012,"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."}}