{"id":"W1588166307","doi":"10.1007/978-3-540-24840-8_61","title":"An Investigation of Grammar Design in Natural-Language Speech Recognition","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Robustness (evolution); Speech recognition; Natural language; Natural language processing; Grammar; Artificial intelligence; Linguistics","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":[0.00324438,0.0003386517,0.0006584062,0.0007149039,0.0007691112,0.002618368,0.001396599,0.00121408,0.003497103],"category_scores_gemma":[0.01957674,0.0009587375,0.0008843161,0.0009658834,0.002926331,0.004811065,0.0009211414,0.00149092,0.0008339504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00118238,"about_ca_system_score_gemma":0.001435669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001898531,"about_ca_topic_score_gemma":0.002065995,"domain_scores_codex":[0.9971728,0.001499978,0.0001932555,0.0004287851,0.0005566441,0.0001483526],"domain_scores_gemma":[0.9844419,0.01274063,0.0005573579,0.001038727,0.001085437,0.000136016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001838494,0.000164894,0.003565839,0.0005577282,0.00004628554,0.0004506347,0.002489615,0.04361534,0.01907668,0.6321135,0.002550651,0.295185],"study_design_scores_gemma":[0.00006313404,0.0001998629,0.001091105,0.0001135441,0.00007591069,0.0006105215,0.0007899485,0.3124144,0.01773544,0.6524757,0.01438504,0.00004545675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05126773,0.0009865143,0.933166,0.0008764832,0.00006973869,0.0001365676,0.00008211443,0.0007821129,0.01263272],"genre_scores_gemma":[0.507715,0.0008948548,0.4843584,0.0002797275,0.00008153151,0.0001301283,0.0002205604,0.0005285927,0.005791249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003497103,"threshold_uncertainty_score":0.01715815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02942735014565632,"score_gpt":0.2518051128363049,"score_spread":0.2223777626906486,"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."}}