{"id":"W4414995823","doi":"10.21203/rs.3.rs-7808032/v1","title":"Mixed Signal Design Using C++ and DSP Acceleration for Low Latency and Secure Speech Systems","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Latency (audio); Digital signal processing; Low latency (capital markets); Voice activity detection; Novelty; Speech processing; Signal processing; Digital signal processor","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002548824,0.0002344265,0.0003591663,0.0005894568,0.000433736,0.001444782,0.0005460598,0.000374804,0.00001772638],"category_scores_gemma":[0.0003511077,0.000221632,0.00007692503,0.0003643809,0.00008239445,0.0002706666,0.0009327797,0.0005845368,0.000005044236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001415626,"about_ca_system_score_gemma":0.0005570885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001525203,"about_ca_topic_score_gemma":0.00001618405,"domain_scores_codex":[0.9968899,0.0007870488,0.0003247392,0.0008415637,0.0006791836,0.0004775603],"domain_scores_gemma":[0.9971877,0.001192196,0.00009716285,0.000463472,0.0008806316,0.0001787884],"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":[0.0003015445,0.0004629515,0.0009973362,0.01941456,0.0004528216,0.0002323812,0.002978666,0.002883506,0.009448982,0.02607506,0.01004373,0.9267085],"study_design_scores_gemma":[0.0004249978,0.0001343372,0.0003256913,0.001911381,0.00001826468,0.00003041832,0.0002419686,0.9777051,0.008902959,0.009475983,0.0004933198,0.000335534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02967463,0.001812721,0.9637206,0.0005193195,0.0004582274,0.003093891,0.0001273916,0.0001344558,0.0004588176],"genre_scores_gemma":[0.6319133,0.001093944,0.3640711,0.00006550741,0.0005681103,0.0008136845,0.0000848612,0.00004908897,0.00134043],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9748216,"threshold_uncertainty_score":0.9995918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2481162879091529,"score_gpt":0.3979784810351982,"score_spread":0.1498621931260454,"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."}}