{"id":"W1829432249","doi":"10.1109/icassp.1994.389344","title":"Application of vector quantized hidden Markov modeling to telephone network based connected digit recognition","year":2002,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Codebook; Hidden Markov model; Speech recognition; Telephone network; Computer science; Telephony; Pattern recognition (psychology); Artificial intelligence; Markov model; Markov chain; Machine learning; Telecommunications","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":[0.0008433087,0.0002411693,0.000336326,0.000286684,0.0001579528,0.0004390816,0.0003746986,0.0003320014,0.001020404],"category_scores_gemma":[0.003858018,0.0002002589,0.0002323323,0.0004010463,0.000277217,0.0004458652,0.0002952613,0.0004008617,0.0002030324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005192482,"about_ca_system_score_gemma":0.0003722012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007169577,"about_ca_topic_score_gemma":0.005556701,"domain_scores_codex":[0.9995907,0.0002200871,0.00002264185,0.00005421562,0.00008768038,0.00002464211],"domain_scores_gemma":[0.9983797,0.001330811,0.00007203522,0.00008623864,0.0001151836,0.00001607725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008314963,0.00003489309,0.001287292,0.00005426789,0.00003167803,0.00007369635,0.00007955181,0.8832506,0.004464572,0.008964742,0.0005805613,0.101095],"study_design_scores_gemma":[0.000001753791,0.000009935817,0.0001538426,0.000001971036,0.000002524688,0.00001041652,0.000003440115,0.9974493,0.0007752337,0.001463775,0.0001247328,0.000002946822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05951813,0.0004214571,0.9378214,0.0002036784,0.00003793551,0.00003069154,0.00009311462,0.000732151,0.001141444],"genre_scores_gemma":[0.8472794,0.0003959918,0.1505732,0.00005500945,0.00002460984,0.00003697939,0.000166596,0.00004915306,0.001419065],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007169577,"threshold_uncertainty_score":0.0142557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04209136715797211,"score_gpt":0.2318102425224035,"score_spread":0.1897188753644314,"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."}}