{"id":"W4285242026","doi":"10.51542/ijscia.v3i3.25","title":"ArmSpeech: Armenian Spoken Language Corpus","year":2022,"lang":"en","type":"article","venue":"International Journal Of Scientific Advances","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Armenian; Stress (linguistics); The Republic; Diaspora; Linguistics; Speech corpus; Spoken language; Identification (biology); History; Computer science; Natural language processing; Artificial intelligence; Political science; Speech synthesis; Law","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":[],"consensus_categories":[],"category_scores_codex":[0.0008489793,0.00007692591,0.0001161626,0.0004539844,0.0002225385,0.0003659541,0.00193877,0.00001120186,0.0006618514],"category_scores_gemma":[0.0001127016,0.00006811324,0.0001205389,0.0003540298,0.00007955518,0.0009697345,0.0002809365,0.0001779518,0.00004261122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001338656,"about_ca_system_score_gemma":0.0001133953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003149453,"about_ca_topic_score_gemma":0.000005182605,"domain_scores_codex":[0.9979686,0.00007040751,0.0003402931,0.0001977557,0.001279951,0.0001430634],"domain_scores_gemma":[0.9988881,0.0001022827,0.0003977222,0.0001690232,0.0003581511,0.0000847324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000457113,0.0002147694,0.0007625065,0.000002438624,0.00006089129,0.0008029544,0.001371105,0.0002783493,0.010319,0.004152535,0.00355475,0.978435],"study_design_scores_gemma":[0.001071256,0.0002045377,0.001262943,0.00003980727,0.00001302896,0.00310906,0.002329925,0.003857595,0.04457498,0.01384972,0.9293659,0.0003212561],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7956835,0.003187833,0.1332428,0.009409476,0.04257952,0.0001935318,0.0001023732,0.0001391932,0.01546179],"genre_scores_gemma":[0.9568743,0.00004144231,0.04000319,0.0004245276,0.0002601666,0.000004803079,0.000004758888,0.000006261469,0.002380555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9781137,"threshold_uncertainty_score":0.7246808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01403120144258311,"score_gpt":0.2771334018677351,"score_spread":0.2631022004251519,"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."}}