{"id":"W1815659836","doi":"","title":"MUSAE Lab Research: From Anthropomorphic Speech Technologies to Human-Machine Interfaces and Health Diagnostics Tools","year":2015,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Natural Sciences and Engineering Research Council of Canada; Ministère de l'Économie, de l’Innovation et des Exportations du Québec; Nuance Foundation; Réseau québécois de recherche sur le vieillissement; Ministère du Développement Économique, de l’Innovation et de l’Exportation; Nvidia","keywords":"Human–machine system; Computer science; Research centre; Engineering management; Engineering; Multimedia; Data science; Telecommunications; Human–computer interaction; Library science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.002862921,0.001351667,0.0007013471,0.001915807,0.0005702013,0.00398034,0.001341738,0.001105967,0.01983326],"category_scores_gemma":[0.002456265,0.0004390616,0.0004223879,0.0009087619,0.001443971,0.00215506,0.001736352,0.0009206289,0.005633785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001763584,"about_ca_system_score_gemma":0.002475905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01600652,"about_ca_topic_score_gemma":0.02808739,"domain_scores_codex":[0.9982613,0.0004306296,0.00005603295,0.0003602545,0.0007523326,0.0001394504],"domain_scores_gemma":[0.9976283,0.0007027526,0.00007174121,0.0002366676,0.001037833,0.0003227356],"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":[0.0004801637,0.0004057521,0.002813064,0.0004436485,0.00008548116,0.0003081044,0.000958576,0.001944516,0.08670409,0.02955115,0.05116373,0.8251418],"study_design_scores_gemma":[0.0002107682,0.001282786,0.01677293,0.0004889943,0.0001377501,0.001227411,0.001662619,0.05215077,0.1052494,0.02750435,0.7931393,0.0001728818],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.06360594,0.02710503,0.7256221,0.01239316,0.002742867,0.0008121053,0.002341889,0.01184078,0.1535362],"genre_scores_gemma":[0.1796876,0.01636074,0.5610704,0.003285876,0.002052258,0.0005850564,0.003275105,0.001853826,0.2318291],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01983326,"threshold_uncertainty_score":0.06634891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2136344214209714,"score_gpt":0.3824580793711415,"score_spread":0.16882365795017,"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."}}