{"id":"W2139231522","doi":"10.1109/icapr.2009.80","title":"Bangla Speech Recognition System Using LPC and ANN","year":2009,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"La Cité Collégiale","funders":"","keywords":"Speech recognition; Computer science; Linear predictive coding; Cepstrum; Speech processing; Voice activity detection; Speech coding; Mel-frequency cepstrum; Artificial intelligence; Pattern recognition (psychology); Vector quantization; Artificial neural network; Feature extraction; Feature vector","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":[0.0003784223,0.0005618415,0.000615549,0.0006353829,0.0004876544,0.0009805131,0.0006986114,0.000687746,0.006426833],"category_scores_gemma":[0.0008412849,0.0002917156,0.0004217511,0.0005630756,0.0002348522,0.0007017225,0.0003468091,0.0005282772,0.004494109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004708857,"about_ca_system_score_gemma":0.0004107424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005331384,"about_ca_topic_score_gemma":0.004063614,"domain_scores_codex":[0.9994728,0.00005133723,0.00005695828,0.0001615317,0.0002194065,0.00003805419],"domain_scores_gemma":[0.999587,0.0000755396,0.00002680376,0.0000403353,0.0002532709,0.00001709345],"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":[0.0004822314,0.000191522,0.003238965,0.000601416,0.0001604435,0.0006873792,0.0002506202,0.05981888,0.1308854,0.002576258,0.01039482,0.7907121],"study_design_scores_gemma":[0.00004479824,0.0003285902,0.006822756,0.0001138832,0.0001370742,0.0009291316,0.000123714,0.8807744,0.06760637,0.001690259,0.04131099,0.000118105],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08134063,0.002088963,0.84914,0.0005187608,0.0006453115,0.0004741183,0.001131869,0.02246067,0.04219957],"genre_scores_gemma":[0.5420069,0.001524277,0.3946761,0.0003851463,0.0001794672,0.0004990546,0.002567031,0.0004043359,0.0577577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006426833,"threshold_uncertainty_score":0.02149993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02663728876712292,"score_gpt":0.24343624757153,"score_spread":0.216798958804407,"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."}}