{"id":"W4390376799","doi":"10.18280/ts.400608","title":"Optimizing Acoustic Feature Selection for Estimating Speaker Traits: A Novel Threshold-Based Approach","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Feature selection; Selection (genetic algorithm); Computer science; Speech recognition; Feature (linguistics); Pattern recognition (psychology); Artificial intelligence; Linguistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001161412,0.0009787206,0.000964109,0.001130684,0.000410543,0.0005312557,0.0007339235,0.0006131659,0.001811982],"category_scores_gemma":[0.002356511,0.0002456757,0.001013153,0.0006661681,0.000239431,0.0005300465,0.000657711,0.0005983026,0.001142484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002457164,"about_ca_system_score_gemma":0.0007390629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003071495,"about_ca_topic_score_gemma":0.003576064,"domain_scores_codex":[0.9993399,0.0001210796,0.00004965648,0.0001813727,0.0002205041,0.00008751613],"domain_scores_gemma":[0.9991907,0.0003528002,0.00004615954,0.00005699844,0.0003216276,0.0000315985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005435123,0.0002396933,0.007988313,0.0001052687,0.0001817358,0.0002473364,0.0001688506,0.04277291,0.104447,0.001148495,0.003431499,0.8387253],"study_design_scores_gemma":[0.00005082795,0.0003214366,0.01650548,0.00002329205,0.0001403624,0.0006067405,0.0001263267,0.934716,0.04079163,0.002187279,0.004476533,0.00005407492],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05508374,0.0004460488,0.9419699,0.000130205,0.00006531856,0.00007220046,0.0002207131,0.001096671,0.0009152314],"genre_scores_gemma":[0.5347579,0.0003653043,0.4559535,0.0001904091,0.0001214845,0.0002599939,0.002010439,0.0003053038,0.006035595],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003071495,"threshold_uncertainty_score":0.006142199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03320050116025036,"score_gpt":0.2631579215462727,"score_spread":0.2299574203860223,"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."}}