{"id":"W3176272988","doi":"10.23641/asha.14167058.v1","title":"Forced alignment of child speech (Mahr et al., 2021)","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Speech recognition; Phone; Two-alternative forced choice; Sample (material); Segmentation; Natural language processing; Artificial intelligence; Linguistics; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00006029272,0.00009230628,0.0001361954,0.00004632877,0.00004303883,0.00007187662,0.000363244,0.00003909679,0.1703415],"category_scores_gemma":[0.0004065032,0.00009040595,0.0001041958,0.0002580613,0.000002870927,0.0001674163,0.0002385118,0.00007684824,0.002176948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001712037,"about_ca_system_score_gemma":0.00007053458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002959854,"about_ca_topic_score_gemma":0.000007417256,"domain_scores_codex":[0.9990666,0.00005523266,0.000166207,0.0002721876,0.0002817328,0.0001580232],"domain_scores_gemma":[0.9991769,0.0001101382,0.00007271638,0.0004291192,0.0001394619,0.00007170551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001150288,0.00005852304,0.000004932414,0.00002392078,0.00001986984,0.00008364562,0.0000705646,0.000002303768,0.0005923693,0.0005555555,0.9012106,0.09737655],"study_design_scores_gemma":[0.0002132687,0.00002227001,0.0004278072,0.0006686194,0.000002709182,0.00006570889,0.00002679403,0.001329659,0.3295394,0.0002586098,0.6672859,0.0001592526],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.001144499,0.001544705,0.004489099,0.06972037,0.0008811699,0.0009512533,0.1531795,0.0005574964,0.7675319],"genre_scores_gemma":[0.3371061,0.0003698428,0.2596259,0.2289279,0.000695543,0.0008684715,0.152227,0.0002128225,0.01996643],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7475654,"threshold_uncertainty_score":0.9985999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03463776890904033,"score_gpt":0.2646876765530524,"score_spread":0.230049907644012,"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."}}