72 citations · 143 across the 6 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
Effective Use of Variational Embedding Capacity in Expressive End-to-End Speech Synthesis
Eric Battenberg, Soroosh Mariooryad, Daisy Stanton +4
Recent work has explored sequence-to-sequence latent variable models for expressive speech synthesis (supporting control and transfer of prosody and style), but has not presented a…
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
RJ Skerry-Ryan, Eric Battenberg, Ying Xiao +6
We present an extension to the Tacotron speech synthesis architecture that learns a latent embedding space of prosody, derived from a reference acoustic representation containing t…
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Yuxuan Wang, Daisy Stanton, Yu Zhang +7
In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system. The emb…