{"id":"W4386896658","doi":"10.1038/s41598-023-42818-3","title":"Comparison of the prediction accuracy of machine learning algorithms in crosslinguistic vowel classification","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Humanities Research Group, University of Windsor; University of Nicosia","keywords":"Formant; Vowel; Linear discriminant analysis; Computer science; Speech recognition; Artificial neural network; Artificial intelligence; Perception; Decision tree; Similarity (geometry); Natural language processing; Psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.01374931,0.001287296,0.0007921042,0.002307676,0.0004303933,0.001658163,0.0006714391,0.001378545,0.0008095885],"category_scores_gemma":[0.04116413,0.0002462073,0.0007482846,0.000909799,0.0004692254,0.001956933,0.0009117055,0.001224026,0.0005619903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007699024,"about_ca_system_score_gemma":0.0007004207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00463768,"about_ca_topic_score_gemma":0.003041069,"domain_scores_codex":[0.995301,0.00247775,0.0005216319,0.0007628941,0.0006522579,0.0002845609],"domain_scores_gemma":[0.9493724,0.04301449,0.001123396,0.00198985,0.004056753,0.0004431679],"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":[0.003277601,0.0007373376,0.1372935,0.0003458116,0.0009201009,0.0001407847,0.0005460624,0.2834795,0.005839184,0.002172295,0.002519948,0.5627279],"study_design_scores_gemma":[0.00004061931,0.0006582569,0.02438181,0.00006154925,0.00009412783,0.00008869314,0.0002064667,0.9670563,0.005474865,0.001270917,0.00060847,0.0000578429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8904706,0.002257612,0.09985906,0.0005466862,0.0002566423,0.0001308366,0.0004241377,0.001119503,0.004934911],"genre_scores_gemma":[0.9671603,0.0003095325,0.03110517,0.00007156737,0.00002871632,0.0000662637,0.0005027756,0.000058513,0.0006970643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01374931,"threshold_uncertainty_score":0.07271421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04664852449512247,"score_gpt":0.3345721311493576,"score_spread":0.2879236066542352,"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."}}