{"id":"W7077062340","doi":"10.1002/sdtp.18336","title":"72‐3: Fully Convolutional Transformer‐Based Speech Emotion Recognition for Automotive Systems","year":2025,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Intertek (Canada)","funders":"","keywords":"Convolutional neural network; Automotive industry; Transformer; Emotion recognition; Benchmark (surveying); Channel (broadcasting); Convolutional code","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":[0.0002643583,0.0007294272,0.0002758734,0.0003038221,0.0001508291,0.0004649502,0.0006691802,0.0004363791,0.006682341],"category_scores_gemma":[0.000427716,0.0002107701,0.0005173526,0.000200231,0.0001864672,0.0005906232,0.0004723411,0.0005737566,0.002639352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004744502,"about_ca_system_score_gemma":0.0005902222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007152369,"about_ca_topic_score_gemma":0.009999716,"domain_scores_codex":[0.9998485,0.00001664133,0.000007582944,0.00003469602,0.00005467197,0.00003775913],"domain_scores_gemma":[0.999917,0.00001636965,0.000005557009,0.00001232038,0.00004066151,0.000007966715],"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":[0.0007611441,0.0001802316,0.001557493,0.0002129896,0.0001576578,0.0001949126,0.00008461998,0.04679493,0.2642765,0.00442097,0.01957059,0.6617879],"study_design_scores_gemma":[0.00002992696,0.0002073022,0.002917481,0.00001844371,0.00006652383,0.00023857,0.00003728607,0.8455642,0.1366378,0.002001253,0.0122476,0.00003368287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06381352,0.0008195898,0.9116782,0.0002455638,0.0002555414,0.0001640285,0.001201112,0.01030118,0.01152134],"genre_scores_gemma":[0.7651891,0.0006107735,0.202326,0.0003940691,0.00007739017,0.0001539839,0.003808983,0.0005074836,0.02693232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007152369,"threshold_uncertainty_score":0.02235466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01271445049129625,"score_gpt":0.2368986934215608,"score_spread":0.2241842429302646,"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."}}