{"id":"W4386105229","doi":"10.1109/is3c57901.2023.00051","title":"Performance Evaluation of Indonesian Language Forced Alignment Using Montreal Forced Aligner","year":2023,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Indonesian; Speech recognition; Transcription (linguistics); Natural language processing; Segmentation; Language model; Process (computing); Speech corpus; Set (abstract data type); Artificial intelligence; Speech synthesis; 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":[0.002271937,0.001937404,0.001147168,0.001205605,0.0009022455,0.001272566,0.001523276,0.00104766,0.007451666],"category_scores_gemma":[0.005194506,0.0003653146,0.0006867629,0.001064832,0.0004375519,0.00151023,0.001092237,0.000831919,0.004574566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009276094,"about_ca_system_score_gemma":0.001742168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05911736,"about_ca_topic_score_gemma":0.05353941,"domain_scores_codex":[0.9980635,0.0005557351,0.0001427213,0.0005854972,0.0004282625,0.000224245],"domain_scores_gemma":[0.9976795,0.0008793849,0.00009369363,0.0002978807,0.0008948604,0.0001546516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003431818,0.0005481908,0.0113844,0.0006055932,0.0004398473,0.0007960899,0.0006613743,0.1219165,0.1216008,0.001263943,0.02111899,0.7162325],"study_design_scores_gemma":[0.000178252,0.001432469,0.01744767,0.00004270087,0.0001843741,0.0005727989,0.0007593122,0.871343,0.09718715,0.000549413,0.01013668,0.0001663235],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6568004,0.004062457,0.2605926,0.0006494689,0.0007989556,0.0004088136,0.003897285,0.04915373,0.02363618],"genre_scores_gemma":[0.7970256,0.0007116636,0.1708946,0.0002676215,0.00008200265,0.0002917331,0.01467012,0.001621317,0.01443535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05911736,"threshold_uncertainty_score":0.1175466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05697551244489023,"score_gpt":0.3030025896258461,"score_spread":0.2460270771809558,"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."}}