{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004689073,0.0001804692,0.0002784125,0.0001089794,0.0001590755,0.00004350232,0.0005104521,0.0002354266,0.00001479657],"category_scores_gemma":[0.0002410576,0.0001731325,0.0001985441,0.000390501,0.0001480267,0.0001568493,0.00004713904,0.0001652511,0.000005716647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009492652,"about_ca_system_score_gemma":0.0002032181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000232662,"about_ca_topic_score_gemma":0.000006401675,"domain_scores_codex":[0.9984645,0.00005188197,0.0004404338,0.0004555213,0.0002499043,0.0003377551],"domain_scores_gemma":[0.9987392,0.0003158775,0.0001463561,0.0003371054,0.0003930325,0.00006841808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008715643,0.0002859343,0.000314255,0.0005772007,0.0000507668,0.000004194912,0.0000419829,0.002505058,0.966365,0.02750525,0.001172168,0.001091066],"study_design_scores_gemma":[0.004973397,0.001422304,0.01570428,0.001862253,0.0002175925,0.00008110928,0.00027966,0.0142708,0.8868781,0.01369024,0.05920845,0.001411815],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.006797534,0.0001312271,0.07630504,0.02459277,0.001143266,0.001831789,0.0001165231,0.0008003807,0.8882815],"genre_scores_gemma":[0.9966879,0.000009925101,0.001879343,0.0002365366,0.0000423456,0.000128777,0.00008326463,0.0000036424,0.0009282745],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9898903,"threshold_uncertainty_score":0.706014,"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."}}