{"id":"W1970415547","doi":"10.2316/journal.206.2007.2.206-2735","title":"CONTINUOUS SPEECH RECOGNITION IN NOISE USING A SPECTRUM-ENTROPY BEAM-FORMER","year":2007,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Speech recognition; Noise spectrum; Acoustics; Computer science; Noise (video); Spectrum (functional analysis); Artificial intelligence; Physics; Noise reduction","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005987945,0.00007535575,0.0001194294,0.0003840648,0.00003594481,0.0002160236,0.0002358475,0.00004160614,0.000005936744],"category_scores_gemma":[0.00007558069,0.0000693376,0.00004083901,0.000161768,0.00001765672,0.0009670933,0.00004335657,0.0001271975,0.000003262281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001110084,"about_ca_system_score_gemma":0.00005594414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001388542,"about_ca_topic_score_gemma":0.0000102348,"domain_scores_codex":[0.9989452,0.00001389363,0.0004365517,0.00009778237,0.0003702322,0.0001362943],"domain_scores_gemma":[0.9991708,0.00005001565,0.0004019383,0.00004951157,0.0002735131,0.00005420056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009498829,0.0002758883,0.02503533,0.00002435284,0.00009094781,0.0006317178,0.0009244303,0.009456518,0.09147411,0.00159085,0.00007384714,0.870327],"study_design_scores_gemma":[0.004750487,0.0004071069,0.1350671,0.001262555,0.00004912584,0.005623715,0.0003016808,0.3326783,0.4532131,0.06559008,0.0004019441,0.0006549215],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.531774,0.00006471264,0.4669322,0.0006329967,0.0004671777,0.00002834353,4.845756e-7,0.00001024504,0.00008982793],"genre_scores_gemma":[0.7883613,0.00004066714,0.211242,0.0001344596,0.0002073985,1.090813e-7,0.00000163935,0.000004084371,0.000008340499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8696721,"threshold_uncertainty_score":0.2827505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01736950141857407,"score_gpt":0.2745872323731533,"score_spread":0.2572177309545792,"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."}}