{"id":"W4293223597","doi":"10.11159/icbes22.141","title":"Myo-Speech: A System for Recognizing Word Utterances of the Speech Impaired","year":2022,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Subtitles and Audiovisual Media","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Communication and Information Technology","keywords":"Speech recognition; Computer science; Word (group theory); Speech synthesis; Audio mining; Speech processing; Natural language processing; Artificial intelligence; Voice activity detection; 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":[0.0006064513,0.0008381982,0.0006917798,0.001028951,0.0002621701,0.0005112981,0.0007008671,0.001003277,0.006408043],"category_scores_gemma":[0.001029439,0.000211084,0.0002827559,0.0003274707,0.000213794,0.0006036144,0.0007889403,0.0003025093,0.005973403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001717428,"about_ca_system_score_gemma":0.0002494612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008904625,"about_ca_topic_score_gemma":0.001817807,"domain_scores_codex":[0.9996106,0.00006440357,0.00003700735,0.000156968,0.00009714629,0.00003383879],"domain_scores_gemma":[0.9995919,0.0001232266,0.0000461304,0.00006645064,0.0001245244,0.00004778552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001799273,0.0002159575,0.01198174,0.0009812382,0.0002215619,0.0006109304,0.0004919863,0.001035151,0.4959191,0.0006333661,0.02072724,0.4653825],"study_design_scores_gemma":[0.0004015618,0.002440739,0.1740173,0.0002532897,0.000483847,0.007686993,0.0008808126,0.1659268,0.548718,0.002275632,0.09648179,0.0004331823],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3516097,0.003210454,0.5460256,0.0002751826,0.0006554177,0.00109113,0.01874574,0.06293867,0.01544812],"genre_scores_gemma":[0.5448773,0.0009495629,0.4098812,0.0005790176,0.000242314,0.001732317,0.01900376,0.001317056,0.02141749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006408043,"threshold_uncertainty_score":0.02143699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01850234468981739,"score_gpt":0.2065631034800389,"score_spread":0.1880607587902215,"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."}}