{"id":"W3210306132","doi":"10.1109/rteict52294.2021.9573848","title":"Emotional Speech Cloning using GANs","year":2021,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Speech recognition; Discriminator; Sadness; Speech synthesis; Context (archaeology); Generator (circuit theory); Cloning (programming); Artificial neural network; Anger; Active listening; Field (mathematics); Natural language processing; Artificial intelligence; Psychology; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005795626,0.0006630509,0.0003735808,0.0001984007,0.0001366101,0.0004419422,0.0005855411,0.0005135718,0.002337092],"category_scores_gemma":[0.001387979,0.0002396114,0.0005518729,0.0001419351,0.0003607183,0.0005984103,0.000617657,0.0009748701,0.0007438914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003392035,"about_ca_system_score_gemma":0.0002490171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001247977,"about_ca_topic_score_gemma":0.001984236,"domain_scores_codex":[0.9997666,0.00006927994,0.000009011122,0.00007068962,0.00005385184,0.00003048103],"domain_scores_gemma":[0.9996635,0.0001932017,0.00001810808,0.00004954826,0.00006208749,0.00001362864],"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":[0.0003216212,0.00009751836,0.001254085,0.0001343227,0.0001198652,0.0002575729,0.0001634062,0.5665167,0.04686299,0.01337044,0.006416279,0.3644852],"study_design_scores_gemma":[0.000006288546,0.00003265893,0.0001672025,0.000007169447,0.00001191944,0.00004495912,0.000009909267,0.9899904,0.005915991,0.002467292,0.001341109,0.00000510687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03320793,0.0005147641,0.958235,0.0002504259,0.0001978547,0.00004980643,0.0001243227,0.002315694,0.005104144],"genre_scores_gemma":[0.7813855,0.0004614733,0.2043511,0.0004716323,0.0001383123,0.000133892,0.0006929591,0.0003533616,0.01201174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002337092,"threshold_uncertainty_score":0.007818401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05172122108534818,"score_gpt":0.2743723135183435,"score_spread":0.2226510924329953,"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."}}