{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001005086,0.00005285822,0.00006513621,0.00004339929,0.00008770418,0.0001352308,0.0001663114,0.00002800491,0.001610606],"category_scores_gemma":[0.00005762178,0.00005078289,0.00005170878,0.0002583031,0.00001020382,0.0002147677,0.00008897558,0.00004862055,0.0002235926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002184549,"about_ca_system_score_gemma":0.00008818624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000078651,"about_ca_topic_score_gemma":0.0000109537,"domain_scores_codex":[0.9993612,0.00003536764,0.0000982977,0.0001980646,0.0001759832,0.0001311519],"domain_scores_gemma":[0.9995791,0.0000584403,0.00001877912,0.0001901017,0.00009993021,0.00005359528],"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":[0.000002032811,0.0002358411,0.003303376,0.0000170218,0.00006023022,0.0008410154,0.0003119769,0.00007055301,0.07149669,0.2161852,0.003767483,0.7037086],"study_design_scores_gemma":[0.0003624544,0.00001488916,0.005806183,0.00005964336,0.00000945226,0.001528685,0.0001856177,0.3022719,0.6650475,0.01319819,0.01110471,0.0004107617],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06823821,0.00002388214,0.8597228,0.00107477,0.000304702,0.00002070986,7.803669e-7,0.0001355953,0.07047854],"genre_scores_gemma":[0.1029477,0.000004651542,0.8931963,0.001207556,0.0000844618,6.951473e-7,0.000001754944,0.000004364183,0.002552547],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7032979,"threshold_uncertainty_score":0.999302,"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."}}