81 citations · 105 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
Mellotron: Multispeaker expressive voice synthesis by conditioning on rhythm, pitch and global style tokens
Rafael Valle, Jason Li, Ryan Prenger +1
Mellotron is a multispeaker voice synthesis model based on Tacotron 2 GST that can make a voice emote and sing without emotive or singing training data. By explicitly conditioning…
WaveGlow: A Flow-based Generative Network for Speech Synthesis
Ryan Prenger, Rafael Valle, Bryan Catanzaro
In this paper we propose WaveGlow: a flow-based network capable of generating high quality speech from mel-spectrograms. WaveGlow combines insights from Glow and WaveNet in order t…
Attacking Speaker Recognition With Deep Generative Models
Wilson Cai, Anish Doshi, Rafael Valle
In this paper we investigate the ability of generative adversarial networks (GANs) to synthesize spoofing attacks on modern speaker recognition systems. We first show that samples…