81 citations · 232 across the 14 of their papers we have counts for
16 papers
VANI: Very-lightweight Accent-controllable TTS for Native and Non-native speakers with Identity Preservation
Rohan Badlani, Akshit Arora, Subhankar Ghosh +5
We introduce VANI, a very lightweight multi-lingual accent controllable speech synthesis system. Our model builds upon disentanglement strategies proposed in RADMMM and supports ex…
Multilingual Multiaccented Multispeaker TTS with RADTTS
Rohan Badlani, Rafael Valle, Kevin J. Shih +3
We work to create a multilingual speech synthesis system which can generate speech with the proper accent while retaining the characteristics of an individual voice. This is challe…
SPACE: Speech-driven Portrait Animation with Controllable Expression
Siddharth Gururani, Arun Mallya, Ting-Chun Wang +2
Animating portraits using speech has received growing attention in recent years, with various creative and practical use cases. An ideal generated video should have good lip sync w…
Generative Modeling for Low Dimensional Speech Attributes with Neural Spline Flows
Kevin J. Shih, Rafael Valle, Rohan Badlani +2
Despite recent advances in generative modeling for text-to-speech synthesis, these models do not yet have the same fine-grained adjustability of pitch-conditioned deterministic mod…
One TTS Alignment To Rule Them All
Rohan Badlani, Adrian Łancucki, Kevin J. Shih +3
Speech-to-text alignment is a critical component of neural textto-speech (TTS) models. Autoregressive TTS models typically use an attention mechanism to learn these alignments on-l…
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
Rafael Valle, Kevin Shih, Ryan Prenger +1
In this paper we propose Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis with control over speech variation and style transfer. Flowtron borr…