{"id":"W2074517850","doi":"10.1007/s11042-014-1973-7","title":"Spectro-temporal directional derivative based automatic speech recognition for a serious game scenario","year":2014,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Speech recognition; Feature (linguistics); Discrete cosine transform; Arabic numerals; Feature vector; Artificial intelligence; Pattern recognition (psychology); Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002605206,0.000154402,0.0001872031,0.0001104527,0.0002313426,0.0002224787,0.0002181297,0.00007468422,0.00009077456],"category_scores_gemma":[0.0001830124,0.0001486442,0.00008022202,0.0002627986,0.0000680446,0.0003012615,0.0000361074,0.00008667295,0.0001218615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003318633,"about_ca_system_score_gemma":0.00006136949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000193858,"about_ca_topic_score_gemma":0.00002758246,"domain_scores_codex":[0.99889,0.00005407824,0.0002508376,0.000412229,0.0001685085,0.0002242816],"domain_scores_gemma":[0.9985902,0.0007273714,0.0001209036,0.0002720872,0.000153169,0.0001363145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000433898,0.00009831009,0.0001675159,0.00002062989,0.00001192252,3.37582e-7,0.00005856096,0.000001494869,0.001016806,0.0007203931,0.0003155262,0.9975842],"study_design_scores_gemma":[0.001416961,0.0001185437,0.01193798,0.00004841623,0.00003472895,0.00002912979,0.0000349206,0.8627175,0.02880942,0.01838755,0.07598978,0.0004750566],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01068525,0.00001949789,0.9844546,0.001689106,0.00008632871,0.001157013,0.00009625118,0.0003310835,0.00148081],"genre_scores_gemma":[0.109208,0.00001007231,0.8873676,0.0006784084,0.0002649174,0.002098955,0.0002275333,0.0000176281,0.0001268589],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9971091,"threshold_uncertainty_score":0.6061535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03068364240253427,"score_gpt":0.2570780568454679,"score_spread":0.2263944144429337,"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."}}