{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001518042,0.002147267,0.001080858,0.00321045,0.001324221,0.001661613,0.001649755,0.001737237,0.0208789],"category_scores_gemma":[0.004123894,0.0004526212,0.000578305,0.003094469,0.0005696141,0.001362523,0.002255165,0.001265114,0.02138389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00111005,"about_ca_system_score_gemma":0.002047336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01451587,"about_ca_topic_score_gemma":0.01342371,"domain_scores_codex":[0.9979802,0.0004974169,0.0003653548,0.0005418339,0.0004680042,0.0001471732],"domain_scores_gemma":[0.9983412,0.0005558425,0.0001039571,0.0003046268,0.0006085684,0.00008568034],"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":[0.001602339,0.0005000737,0.003143474,0.005038108,0.0002040742,0.002432882,0.00192447,0.004086885,0.02645893,0.003363115,0.7594019,0.1918438],"study_design_scores_gemma":[0.0007148543,0.0002867782,0.05442372,0.0008470048,0.0002213849,0.001984495,0.002155459,0.01254566,0.01508087,0.002721312,0.9087657,0.0002527697],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.06129198,0.00331603,0.01059855,0.0006227666,0.0008870236,0.001230871,0.8957639,0.00602874,0.02026009],"genre_scores_gemma":[0.03799022,0.000666282,0.01098086,0.0001703392,0.0001042684,0.002172109,0.9411908,0.0004824964,0.00624264],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0208789,"threshold_uncertainty_score":0.06984693,"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."}}