{"id":"W3211192538","doi":"10.17023/47x2-c959","title":"Speech Emotion Recognition Using Quaternion Convolutional Neural Networks","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Speech recognition; Convolutional neural network; Spectrogram; Quaternion; Emotion recognition; Motion capture; Artificial intelligence; Session (web analytics); Pattern recognition (psychology); Motion (physics); Mathematics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002768555,0.0003528101,0.0003401851,0.0003006173,0.000200156,0.00008558958,0.00023264,0.0007904104,0.002767267],"category_scores_gemma":[0.00002559654,0.0004636899,0.0003373148,0.000365235,0.0001178717,0.0002322236,0.0002918009,0.0009269275,0.0002315803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000426786,"about_ca_system_score_gemma":0.0001019449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006772167,"about_ca_topic_score_gemma":0.0001222613,"domain_scores_codex":[0.9975032,0.000597977,0.0003200726,0.001073604,0.00009671276,0.0004084149],"domain_scores_gemma":[0.9985287,0.00006459469,0.0003811613,0.0004720284,0.000389188,0.000164296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009884544,0.001540919,0.01803023,0.0003705514,0.001351973,0.003064804,0.00144698,0.9103563,0.0005142744,0.00645425,0.001749094,0.05413211],"study_design_scores_gemma":[0.001549015,0.00008551266,0.01186365,0.0003060771,0.0004201883,0.0001717521,0.001307661,0.9790151,0.00005885993,0.004355877,0.00007037345,0.0007959402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7838542,0.00008994029,0.208211,0.00003397221,0.004380174,0.0003149936,0.00004720939,0.0001787099,0.00288976],"genre_scores_gemma":[0.9956124,0.0001117035,0.0003303147,0.000181146,0.000637971,0.000001641461,0.001808634,0.00004478211,0.001271385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2117582,"threshold_uncertainty_score":0.9997815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1777273467799341,"score_gpt":0.2457624135832956,"score_spread":0.06803506680336144,"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."}}