activity
20172021
most citedUncovering Latent Style Factors for Expressive Speech Synthesis

44 citations · 86 across the 5 of their papers we have counts for

collaborators

13 papers

cs.SD20211 cited

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…

cs.CL2020

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…

cs.LG2020

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…

cs.CL201915 cited

Semi-Supervised Generative Modeling for Controllable Speech Synthesis

Raza Habib, Soroosh Mariooryad, Matt Shannon +5

We present a novel generative model that combines state-of-the-art neural text-to-speech (TTS) with semi-supervised probabilistic latent variable models. By providing partial super…

cs.CL2019

Location-Relative Attention Mechanisms For Robust Long-Form Speech Synthesis

Eric Battenberg, RJ Skerry-Ryan, Soroosh Mariooryad +4

Despite the ability to produce human-level speech for in-domain text, attention-based end-to-end text-to-speech (TTS) systems suffer from text alignment failures that increase in f…

cs.CL201926 cited

Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning

Yu Zhang, Ron J. Weiss, Heiga Zen +6

We present a multispeaker, multilingual text-to-speech (TTS) synthesis model based on Tacotron that is able to produce high quality speech in multiple languages. Moreover, the mode…