{"id":"W2132638020","doi":"10.1109/icassp.1990.115824","title":"A 450 b.p.s. vocoder with natural-sounding speech","year":2002,"lang":"en","type":"article","venue":"International Conference on Acoustics, Speech, and Signal Processing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Vector quantization; Speech recognition; Computer science; Speech coding; Quantization (signal processing); Linear predictive coding; Coding (social sciences); Data compression; Term (time); Algorithm; Artificial intelligence; Mathematics; Physics","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":[0.0003042145,0.0005780686,0.0005996738,0.0004598384,0.0004469879,0.0006936673,0.0006938378,0.0008933521,0.01728689],"category_scores_gemma":[0.000432066,0.000204718,0.0003804184,0.0004736464,0.0001614819,0.0002958166,0.0003338999,0.0006193214,0.008754338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003889678,"about_ca_system_score_gemma":0.0006624363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00576303,"about_ca_topic_score_gemma":0.009642214,"domain_scores_codex":[0.9997726,0.00003096223,0.00001914884,0.00004642408,0.00008630157,0.00004454443],"domain_scores_gemma":[0.9998559,0.00002837277,0.000004852422,0.00001799027,0.0000726819,0.0000201045],"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":[0.0007472959,0.0003593305,0.0005546823,0.0003989474,0.0001259239,0.0004121988,0.0001778479,0.01294851,0.3870381,0.01183511,0.03477676,0.5506252],"study_design_scores_gemma":[0.0003006194,0.00100195,0.003998043,0.000109001,0.0001973467,0.001592711,0.0001285529,0.4384431,0.2937648,0.004021438,0.2563393,0.0001031942],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06555513,0.0009355897,0.856586,0.0004560667,0.0005810424,0.001309642,0.002559517,0.01750438,0.0545126],"genre_scores_gemma":[0.315192,0.001138372,0.5695724,0.001105078,0.0002778439,0.001098815,0.003942911,0.001036293,0.1066362],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01728689,"threshold_uncertainty_score":0.05783045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0376488748059052,"score_gpt":0.2885850859963695,"score_spread":0.2509362111904643,"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."}}