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The temporal structures can be designed either on the\nspeech side or the text side, leading to different control resolutions in time.\nThe prosody embedding networks are plugged into end-to-end speech synthesis\nnetworks and trained without any other supervision except for the target speech\nfor synthesizing. It is demonstrated that the prosody embedding networks\nlearned to extract prosodic features. By adjusting the learned prosody\nfeatures, we could change the pitch and amplitude of the synthesized speech\nboth at the frame level and the phoneme level. We also introduce the temporal\nnormalization of prosody embeddings, which shows better robustness against\nspeaker perturbations during prosody transfer tasks.","url_abs":"http://arxiv.org/abs/1811.02122v2","url_pdf":"http://arxiv.org/pdf/1811.02122v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"robust-and-fine-grained-prosody-control-of","repo_url":"https://github.com/keonlee9420/Robust_Fine_Grained_Prosody_Control","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"expressive-speech-synthesis","task_name":"Expressive Speech Synthesis"},{"task_slug":"speech-synthesis","task_name":"Speech Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1811.02122","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.02122"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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