{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001019834,0.0007556248,0.000702242,0.0008787045,0.0003700988,0.0008056567,0.001063548,0.0009738197,0.003998381],"category_scores_gemma":[0.002608497,0.0003701369,0.0006807807,0.000918006,0.0004782874,0.001819845,0.0009351493,0.001459158,0.001186505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008597194,"about_ca_system_score_gemma":0.0006518622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005035965,"about_ca_topic_score_gemma":0.006328187,"domain_scores_codex":[0.9997188,0.00007137707,0.00002018169,0.00007764187,0.00008308551,0.00002888686],"domain_scores_gemma":[0.9990258,0.0006190318,0.0000703025,0.0001251868,0.0001254504,0.00003435075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002630725,0.0001100382,0.00069976,0.00008589659,0.00005306024,0.000134336,0.00003423857,0.780177,0.002541768,0.01849438,0.004488664,0.1929177],"study_design_scores_gemma":[0.000001601281,0.000008097339,0.00005518386,0.000002328465,0.000002182989,0.000007800714,0.000002846841,0.9922948,0.0002625947,0.007039926,0.0003202262,0.000002357786],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02782921,0.0006238833,0.96495,0.0006082384,0.0001083869,0.00005191305,0.0005408562,0.001598183,0.003689251],"genre_scores_gemma":[0.687034,0.001055079,0.2890631,0.0004934511,0.0002085937,0.000298787,0.002847477,0.0003069412,0.01869253],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005035965,"threshold_uncertainty_score":0.01337588,"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."}}