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Found 10 papers, 6 papers with code
TRACE: A Time-Relational Approximate Cubing Engine for Fast Data Insights
A large class of data questions can be modeled as identifying important slices of data driven by user defined metrics.
Examining Scaling and Transfer of Language Model Architectures for Machine Translation
Natural language understanding and generation models follow one of the two dominant architectural paradigms: language models (LMs) that process concatenated sequences in a single stack of layers, and encoder-decoder models (EncDec) that utilize separate layer stacks for input and output processing.
PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTS
This paper introduces PnG BERT, a new encoder model for neural TTS.
Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling
This paper presents Non-Attentive Tacotron based on the Tacotron 2 text-to-speech model, replacing the attention mechanism with an explicit duration predictor.
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
Hierarchical Generative Modeling for Controllable Speech Synthesis
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 data, such as speaking style, accent, background noise, and recording conditions.
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Clone a voice in 5 seconds to generate arbitrary speech in real-time
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text.
Ranked #2 on
Speech Synthesis
on North American English
In Teacher We Trust: Learning Compressed Models for Pedestrian Detection
It has been observed that many of the parameters of a large network are redundant, allowing for the possibility of learning a smaller network that mimics the outputs of the large network through a process called Knowledge Distillation.
