{"id":"W2024997154","doi":"10.1145/2388676.2388784","title":"Elastic net for paralinguistic speech recognition","year":2012,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Paralanguage; Interpretability; Computer science; Sparse approximation; Representation (politics); Generalization; Speech recognition; Benchmark (surveying); Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Natural language processing; Mathematics","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":[0.0001867282,0.00006749148,0.00006906255,0.00003956636,0.00008129852,0.00009183493,0.0001919859,0.00002947653,0.00004386002],"category_scores_gemma":[0.0002259019,0.00005726391,0.00003101958,0.0001247041,0.00001014789,0.0003788611,0.00003685484,0.00003696726,0.000242182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001373372,"about_ca_system_score_gemma":0.0000207905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003353911,"about_ca_topic_score_gemma":0.000001426797,"domain_scores_codex":[0.9993603,0.000009215657,0.0001062099,0.0001356614,0.00009027064,0.0002983349],"domain_scores_gemma":[0.9995632,0.0001063726,0.0000384212,0.0001338916,0.00006778734,0.00009034302],"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":[0.000008705645,0.0001027032,0.00155232,0.00005602627,0.00001204162,0.000002842747,0.0002207603,0.000004810119,0.002974057,0.004517088,0.008079165,0.9824695],"study_design_scores_gemma":[0.001094855,0.0002138726,0.00283705,0.00008995691,0.00003904393,0.0001318769,0.00005047898,0.01235778,0.8293214,0.1003278,0.05277334,0.0007624848],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01759557,0.0001247283,0.9723154,0.0002482626,0.0007390143,0.0001067052,0.000001336405,0.0001806444,0.008688357],"genre_scores_gemma":[0.4941882,0.000001931606,0.5045049,0.0004733316,0.0004018132,0.0000133279,0.000004630195,0.000004777202,0.0004070066],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.981707,"threshold_uncertainty_score":0.311284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04391533041003614,"score_gpt":0.2826037645132717,"score_spread":0.2386884341032356,"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."}}