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Controllable Prosody Generation With Partial Inputs

arXiv 2303.09446 ↗First posted 14 Mar 2023eess.AS cs.AI cs.CLcited by 1

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Abstract (arXiv)

We address the problem of human-in-the-loop control for generating prosody in the context of text-to-speech synthesis. Controlling prosody is challenging because existing generative models lack an efficient interface through which users can modify the output quickly and precisely. To solve this, we introduce a novel framework whereby the user provides partial inputs and the generative model generates the missing features. We propose a model that is specifically designed to encode partial prosodic features and output complete audio. We show empirically that our model displays two essential qualities of a human-in-the-loop control mechanism: efficiency and robustness. With even a very small number of input values (~4), our model enables users to improve the quality of the output significantly in terms of listener preference (4:1).

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 27 references that are in the map. First posted on arXiv 14 Mar 2023.

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16 of 27 references.

References · 16

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  11. 2020Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesiscited by 41
  12. 2022SpeechPainter: Text-conditioned Speech Inpaintingcited by 17
4 more
  1. 2020CAMP: a Two-Stage Approach to Modelling Prosody in Contextcited by 12
  2. 2020Prosodic Representation Learning and Contextual Sampling for Neural Text-to-Speechcited by 10
  3. 2021Ctrl-P: Temporal Control of Prosodic Variation for Speech Synthesiscited by 8
  4. 2019Using generative modelling to produce varied intonation for speech synthesiscited by 7

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