63.6k citations · 65.7k across the 31 of their papers we have counts for
34 papers
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR
Gary Wang, Ekin D. Cubuk, Andrew Rosenberg +6
Data augmentation is a ubiquitous technique used to provide robustness to automatic speech recognition (ASR) training. However, even as so much of the ASR training process has beco…
WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis
Nanxin Chen, Yu Zhang, Heiga Zen +4
This paper introduces WaveGrad 2, a non-autoregressive generative model for text-to-speech synthesis. WaveGrad 2 is trained to estimate the gradient of the log conditional density…
Sparse, Efficient, and Semantic Mixture Invariant Training: Taming In-the-Wild Unsupervised Sound Separation
Scott Wisdom, Aren Jansen, Ron J. Weiss +2
Supervised neural network training has led to significant progress on single-channel sound separation. This approach relies on ground truth isolated sources, which precludes scalin…
Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis
Ron J. Weiss, RJ Skerry-Ryan, Eric Battenberg +2
We describe a sequence-to-sequence neural network which directly generates speech waveforms from text inputs. The architecture extends the Tacotron model by incorporating a normali…
Parallel Tacotron: Non-Autoregressive and Controllable TTS
Isaac Elias, Heiga Zen, Jonathan Shen +4
Although neural end-to-end text-to-speech models can synthesize highly natural speech, there is still room for improvements to its efficiency and naturalness. This paper proposes a…
WaveGrad: Estimating Gradients for Waveform Generation
Nanxin Chen, Yu Zhang, Heiga Zen +3
This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density. The model is built on prior work on score matching and di…