2.2k citations · 3.2k across the 14 of their papers we have counts for
15 papers
SequenceLayers: Sequence Processing and Streaming Neural Networks Made Easy
RJ Skerry-Ryan, Julian Salazar, Soroosh Mariooryad +8
We introduce a neural network layer API and library for sequence modeling, designed for easy creation of sequence models that can be executed both layer-by-layer (e.g., teacher-for…
Robust and Unbounded Length Generalization in Autoregressive Transformer-Based Text-to-Speech
Eric Battenberg, RJ Skerry-Ryan, Daisy Stanton +4
Autoregressive (AR) Transformer-based sequence models are known to have difficulty generalizing to sequences longer than those seen during training. When applied to text-to-speech…
Learning the joint distribution of two sequences using little or no paired data
Soroosh Mariooryad, Matt Shannon, Siyuan Ma +5
We present a noisy channel generative model of two sequences, for example text and speech, which enables uncovering the association between the two modalities when limited paired d…
Speaker Generation
Daisy Stanton, Matt Shannon, Soroosh Mariooryad +4
This work explores the task of synthesizing speech in nonexistent human-sounding voices. We call this task "speaker generation", and present TacoSpawn, a system that performs compe…
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…
Non-saturating GAN training as divergence minimization
Matt Shannon, Ben Poole, Soroosh Mariooryad +5
Non-saturating generative adversarial network (GAN) training is widely used and has continued to obtain groundbreaking results. However so far this approach has lacked strong theor…