{"id":"W4213234291","doi":"10.1186/s13634-022-00844-9","title":"Free resources for forced phonetic alignment in Brazilian Portuguese based on Kaldi toolkit","year":2022,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Pró-Reitoria de Pesquisa e Pós-Graduação, Universidade Federal do Pará; Nvidia; Universidade Federal do Pará; Fundação Amazônia Paraense de Amparo à Pesquisa; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Scripting language; Speech recognition; Phone; Portuguese; Natural language processing; Process (computing); Intersection (aeronautics); Artificial intelligence; Brazilian Portuguese; Linguistics; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002364816,0.002305056,0.001231242,0.002532437,0.001178741,0.00210195,0.002906937,0.001256114,0.02631115],"category_scores_gemma":[0.01274291,0.001301971,0.001183349,0.001753139,0.0008497949,0.003562519,0.005619018,0.002232013,0.02561386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009873088,"about_ca_system_score_gemma":0.002608476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009823193,"about_ca_topic_score_gemma":0.01320727,"domain_scores_codex":[0.9973136,0.0005642099,0.0005102416,0.000785894,0.0006174047,0.0002086414],"domain_scores_gemma":[0.9953097,0.00165104,0.00026349,0.001658462,0.0008810098,0.0002363559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002698827,0.0003952894,0.00626843,0.003994049,0.0002817364,0.002709001,0.00339775,0.02129375,0.06478884,0.01880636,0.2664492,0.6089168],"study_design_scores_gemma":[0.0005135132,0.0003094556,0.01117657,0.0007288443,0.0002156822,0.002164122,0.001255551,0.1699412,0.1366184,0.02471159,0.6516851,0.0006800356],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02897206,0.001454632,0.4550397,0.0006465003,0.0006316982,0.0006996224,0.07955144,0.4085211,0.02448328],"genre_scores_gemma":[0.1938537,0.0007472759,0.4730629,0.0005170048,0.0001156431,0.001949134,0.2674324,0.05037446,0.01194739],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02631115,"threshold_uncertainty_score":0.08801961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01765056422509273,"score_gpt":0.2759942747064355,"score_spread":0.2583437104813427,"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."}}