{"id":"W1519446688","doi":"10.1109/isspit.2004.1433721","title":"Acoustic training system for speaker independent continuous Arabic speech recognition system","year":2005,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Bigram; Computer science; Speech recognition; Word error rate; Acoustic model; Vocabulary; Context (archaeology); Grammar; Natural language processing; Artificial intelligence; Language model; Test set; Word (group theory); Speaker recognition; Context-free grammar; Set (abstract data type); Hidden Markov model; Rule-based machine translation; Speech processing; Linguistics; Programming language","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":[0.0005210176,0.0005651116,0.0005503595,0.0003760965,0.0003570896,0.0004533519,0.0007617053,0.000545333,0.0136583],"category_scores_gemma":[0.001306101,0.0002981685,0.0002473968,0.0001751963,0.0001538793,0.0005657161,0.0004853343,0.0006578392,0.009333178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002485462,"about_ca_system_score_gemma":0.0004296504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002065196,"about_ca_topic_score_gemma":0.001700796,"domain_scores_codex":[0.9995995,0.00006022123,0.00002900009,0.0001094467,0.00017176,0.00003010762],"domain_scores_gemma":[0.9993823,0.0001151797,0.00002371161,0.00008505525,0.0003427955,0.0000510014],"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":[0.0008701734,0.0002301701,0.002689432,0.0003286239,0.00006984286,0.0003741095,0.0003135353,0.01040383,0.3880424,0.001227229,0.01362494,0.5818258],"study_design_scores_gemma":[0.0002268991,0.001301909,0.01157317,0.0001008406,0.0003006205,0.001716834,0.000224092,0.5942096,0.3238869,0.001596232,0.06468258,0.0001804121],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07371607,0.0004554212,0.8866769,0.0002593055,0.0004133874,0.0004523744,0.0009116645,0.02869065,0.008424256],"genre_scores_gemma":[0.562706,0.0003509471,0.4053896,0.0003591301,0.000227078,0.0009324672,0.003621035,0.000603236,0.02581042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0136583,"threshold_uncertainty_score":0.04569161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04519592834410268,"score_gpt":0.2445552143919086,"score_spread":0.199359286047806,"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."}}