Publications (13)
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Ye Jia, Yu Zhang, Ron J. Weiss +8
We describe a neural network-based system for text-to-speech (TTS) synthesis that is able to generate speech audio in the voice of many different speakers, including those unseen d…
PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTS
Ye Jia, Heiga Zen, Jonathan Shen +2
This paper introduces PnG BERT, a new encoder model for neural TTS. This model is augmented from the original BERT model, by taking both phoneme and grapheme representations of tex…
Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling
Isaac Elias, Heiga Zen, Jonathan Shen +4
This paper introduces Parallel Tacotron 2, a non-autoregressive neural text-to-speech model with a fully differentiable duration model which does not require supervised duration si…
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
Jonathan Shen, Ruoming Pang, Ron J. Weiss +10
This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature predictio…
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…
Neural Program Synthesis with Priority Queue Training
Daniel A. Abolafia, Mohammad Norouzi, Jonathan Shen +2
We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We employ an it…
In Teacher We Trust: Learning Compressed Models for Pedestrian Detection
Jonathan Shen, Noranart Vesdapunt, Vishnu N. Boddeti +1
Deep convolutional neural networks continue to advance the state-of-the-art in many domains as they grow bigger and more complex. It has been observed that many of the parameters o…
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Jonathan Shen, Patrick Nguyen, Yonghui Wu +88
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…
Hierarchical Generative Modeling for Controllable Speech Synthesis
Wei-Ning Hsu, Yu Zhang, Ron J. Weiss +9
This paper proposes a neural sequence-to-sequence text-to-speech (TTS) model which can control latent attributes in the generated speech that are rarely annotated in the training d…
Examining Scaling and Transfer of Language Model Architectures for Machine Translation
Biao Zhang, Behrooz Ghorbani, Ankur Bapna +4
Natural language understanding and generation models follow one of the two dominant architectural paradigms: language models (LMs) that process concatenated sequences in a single s…
Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling
Jonathan Shen, Ye Jia, Mike Chrzanowski +4
This paper presents Non-Attentive Tacotron based on the Tacotron 2 text-to-speech model, replacing the attention mechanism with an explicit duration predictor. This improves robust…
TRACE: A Time-Relational Approximate Cubing Engine for Fast Data Insights
Suharsh Sivakumar, Jonathan Shen, Rajat Monga
A large class of data questions can be modeled as identifying important slices of data driven by user defined metrics. This paper presents TRACE, a Time-Relational Approximate Cubi…
Training Text-To-Speech Systems From Synthetic Data: A Practical Approach For Accent Transfer Tasks
Lev Finkelstein, Heiga Zen, Norman Casagrande +9
Transfer tasks in text-to-speech (TTS) synthesis - where one or more aspects of the speech of one set of speakers is transferred to another set of speakers that do not feature thes…