{"id":"W2169817815","doi":"10.1109/ccece.2000.849723","title":"Optimal feature vector for speech recognition of unequally segmented spoken digits","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Pattern recognition (psychology); Wavelet; Speech recognition; Computer science; Artificial intelligence; Segmentation; Biorthogonal system; Discrete wavelet transform; Wavelet transform; Feature vector; Feature (linguistics); Artificial neural network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003080507,0.0003675976,0.0005037956,0.000341345,0.0001313797,0.0003748698,0.0003418775,0.0003814804,0.001786936],"category_scores_gemma":[0.0007549631,0.0001516576,0.0003236126,0.0003495297,0.0001643565,0.0005382929,0.0002085352,0.0003556201,0.0006565455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003705392,"about_ca_system_score_gemma":0.000478789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002209685,"about_ca_topic_score_gemma":0.001797566,"domain_scores_codex":[0.999759,0.00003931323,0.00001844774,0.00006661277,0.00008707253,0.0000294965],"domain_scores_gemma":[0.9998763,0.00003949421,0.00001260009,0.00001438097,0.00005174334,0.00000547406],"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":[0.0004426527,0.000128896,0.001072614,0.0001853588,0.00004550621,0.0001219745,0.00006847866,0.2210019,0.06957665,0.01166697,0.003140146,0.6925489],"study_design_scores_gemma":[0.00001093199,0.0000899343,0.0007357626,0.00000664036,0.00001445157,0.00005968874,0.000008598176,0.9866131,0.009226155,0.001686913,0.001536585,0.00001122658],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02742075,0.0003261187,0.9709122,0.00007622796,0.00004759954,0.00002594562,0.0001397256,0.0004644879,0.0005869267],"genre_scores_gemma":[0.615734,0.0004818235,0.378682,0.00006742602,0.00005594287,0.0001950402,0.0008212494,0.00009536572,0.003867131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002209685,"threshold_uncertainty_score":0.005977929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04674398843929461,"score_gpt":0.2770880181714566,"score_spread":0.2303440297321619,"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."}}