{"id":"W2135922901","doi":"10.1109/icassp.1985.1168119","title":"Multi-speaker computer recognition of ten connectedly spoken letters","year":2005,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Speech recognition; Computer science; Set (abstract data type); Speaker recognition; Natural language processing; Artificial intelligence; Linguistics; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003549574,0.0004027952,0.0004719878,0.0003271988,0.0003249816,0.0003895563,0.0004658423,0.0004405412,0.005190858],"category_scores_gemma":[0.001199233,0.0001550795,0.0002049442,0.0002421945,0.0002131335,0.0003558129,0.0003958164,0.0003642689,0.001434454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001861761,"about_ca_system_score_gemma":0.000247653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001654861,"about_ca_topic_score_gemma":0.003138683,"domain_scores_codex":[0.9996498,0.00007491372,0.00001508606,0.0001425387,0.00008201606,0.00003561268],"domain_scores_gemma":[0.9993231,0.000326171,0.00003667808,0.00007942391,0.0001574118,0.00007717972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00148943,0.0003631633,0.007497736,0.0003337237,0.000117963,0.0006572047,0.001044258,0.003589302,0.4918813,0.0005168265,0.001956277,0.4905528],"study_design_scores_gemma":[0.0001472992,0.002242277,0.1116573,0.00003096896,0.0002692572,0.003010853,0.001310232,0.2024635,0.6692557,0.00122237,0.00822717,0.0001629725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.895779,0.0001693795,0.09708498,0.00006689574,0.00008351726,0.0001741094,0.0006821823,0.002234851,0.003725071],"genre_scores_gemma":[0.9182757,0.00007907089,0.07536309,0.0000400672,0.00002690059,0.0001105577,0.001173655,0.0001123512,0.004818466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005190858,"threshold_uncertainty_score":0.0173651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01921137036099671,"score_gpt":0.2567711187428244,"score_spread":0.2375597483818277,"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."}}