{"id":"W1687290598","doi":"10.1109/icassp.1988.197155","title":"Design and evolution of a powerful pocket-sized DSP speech processing system for a cochlear implant and other hearing prosthesis applications","year":2003,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Cochlear implant; Computer science; Software; Extractor; Coding (social sciences); Chip; Speech recognition; Computer hardware; Digital signal processing; Linear predictive coding; Speech processing; Engineering; Audiology; Operating system; Telecommunications; Medicine","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":[0.0005383042,0.0004041876,0.000293515,0.000415548,0.0002081959,0.0004099352,0.0009413007,0.0004007822,0.006574597],"category_scores_gemma":[0.0006070397,0.0002246022,0.0002459958,0.0002335998,0.0001693566,0.0004686778,0.0002975406,0.0004574696,0.003014416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002318787,"about_ca_system_score_gemma":0.000612203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001264672,"about_ca_topic_score_gemma":0.001238209,"domain_scores_codex":[0.999783,0.00002110685,0.00001670849,0.0000480808,0.0001059314,0.0000251834],"domain_scores_gemma":[0.9995629,0.00004045144,0.00002268051,0.00007236615,0.0002356029,0.00006609516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000480769,0.0002141619,0.002389062,0.0002307855,0.00005215501,0.0003081767,0.0002546259,0.008635396,0.4387473,0.004302512,0.01193108,0.532454],"study_design_scores_gemma":[0.0005853408,0.004488762,0.01973754,0.0001034366,0.0003756832,0.004121508,0.0001793698,0.2661243,0.4314504,0.003187751,0.2694591,0.0001866907],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.069889,0.0005742233,0.9021074,0.0004388813,0.0002789002,0.0005010404,0.0004354207,0.01245624,0.01331898],"genre_scores_gemma":[0.297062,0.0004075018,0.6688143,0.0004588756,0.0001304907,0.0002581265,0.001036445,0.0008750022,0.03095715],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006574597,"threshold_uncertainty_score":0.02199423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02014457155732657,"score_gpt":0.2500550113502005,"score_spread":0.229910439792874,"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."}}